The Potential of Integrating Artificial Intelligence (AI) With Robotic Surgery
Abstract
This project investigates the potential of integrating Artificial Intelligence (AI) with robotic surgery to develop intelligent medical tools to enhance surgical outcomes and revolutionize healthcare. The objective is to examine the current landscape of robotic surgical procedures, identify the role of AI in improving the accuracy and efficiency of these procedures, and evaluate the challenges and limitations of this approach. The project will give me a chance to generate analytical research and analytical skills, merge with the group, and gain a deeper understanding of AI’s application in robotic surgery. The project report will include a comprehensive analysis of the existing research on AI-assisted robotic surgery, a discussion of the merits and demerits of this system, and knowledge gained from the project. By combining AI with robotic surgery, this plan can generate the lifecare company by offering surgeons advanced tools capable of making more precise and data-driven decisions, ultimately improving patient outcomes and reducing recovery times.
1. Introduction:
1.1. Problem Definition
In the lifecare industry, the decision by medics is challenging for the proper synthesis and cure. But, the large portion of data ready can make doctors happy, which makes it a challenge to give appropriate care. Superb medical equipment that uses Artificial Intelligence (AI) has the proven ability to help doctors scrutinize more detailed data hence informing decisions. However, we still need to examine the ability applications of AI in superb medical equipment to improve medical decision-making processes.
1.2. Objectives
The main goal of this plan is to determine whether the use of AI in developing superb medical equipment for doctors increases the essence of medical decision-making processes. The plan aims to examine the original research on this ground, find prospective use cases for AI, discuss the current state of research, find loopholes, and suggest potential solutions. It also targets to give a chance for learners to generate their project and analytical skills, merge with a group, and gain knowledge on the application of AI in the healthcare industry.
1.3. The outline of the report
The outline of the project for this plan is grouped into three main categories. The first category will focus on the current landscape of robotic surgical procedures, providing an overview of existing robotic surgical systems and the role of AI in enhancing their capabilities. This category tends to evaluate the merits and demerits of using AI in robotic surgery, focusing on their feasibility and capabilities.
The second category will ensure it concentrates on the suggested solution, beginning with a previous theory category that gives an overview of the suggested AI technology and potential gains for robotic surgery. This category will give stages that show the implementation of the final suggested technology, together with any necessary technical qualifications and resources needed. It also leads to delving into the ethical considerations and potential challenges of AI-assisted robotic surgery.
Lastly, the report will discuss the proposed solution, including its potential impact on healthcare, surgical outcomes, and patient recovery times. Furthermore, it will consider any potential limitations or challenges that may arise while implementing and adopting AI-integrated robotic surgery systems. Generally, the project will give a more detailed analysis of the capabilities and limits generated in integrating AI into robotic surgery systems to improve surgical outcomes. The report aims to offer well-designed knowledge of this technology’s potential advantages and disadvantages. It suggests recommendations that can be applied in future research and development in this plan.
2. Available solutions
2.1. Brainstorming
The brainstorming section aims to show solutions for integrating AI into robotic surgery systems to develop smart medical tools to improve surgical outcomes. Some potential solutions include using AI for preoperative planning and surgical simulation, real-time intraoperative guidance, automated suturing, and postoperative monitoring and recovery management. Additionally, AI can optimize the performance of robotic surgical systems, improve surgeon training, and predict potential complications. (Haick et al., 2021; Bramhe & Pathak, 2022)
2.2. Merits and Demerits of each Solution
Using AI, preoperative planning, and surgical simulation can help optimize surgical plans, reducing operative time and minimizing complications. However, this approach may require large datasets and constant updates to maintain accuracy. Real-time intraoperative guidance using AI can help surgeons make precise decisions during surgery, improving patient outcomes. However, it may rely on high-quality data, and the integration of AI might introduce latency concerns. (Bramhe & Pathak, 2022)
Automated suturing using AI can increase the speed and consistency of suturing during robotic surgery, leading to faster recovery times and reduced scarring. However, it may require extensive validation and rigorous testing to ensure patient safety. Postoperative monitoring and recovery management using AI can help identify potential complications early, leading to perfect patient results. Nevertheless, it may lead to a rise in privacy related to and need for specialized technical skills. (Haick et al., 2021)
Optimizing the performance of robotic surgical systems using AI can lead to more efficient surgeries and reduced operative times. However, developing algorithms to optimize performance may take time and effort. AI-assisted surgeon training can enhance the learning process, leading to better-prepared surgeons. However, this approach may require significant investment in specialized training resources and continuous updates to reflect the latest surgical techniques and practices. (Bramhe & Pathak, 2022)
3. The suggested solution
3.1. Background and Theory
The suggested solution for integrating AI into robotic surgery systems is an AI-assisted surgical system that can assist surgeons in making precise and data-driven decisions during surgery. The plan will be facilitated by an algorithm learning machine that can examine real-time information from various sources, like medical imaging and surgical instruments, to give accurate and personalized guidance to surgeons. The plan will be made in a way that is user-friendly and accessible with easy for surgeons, with recent updates and alarms for capable risks and complications.
3.2. Steps of implementation of the final proposed technology
The first stage in executing the AI-assisted surgical plan is to collect and examine real-time information from other sources, such as medical imaging and surgical instruments. The information is pre-processed and cleared to ensure it is correct and consistent. Apparatus to algorithm learning, like deep neural networks and convolutional neural networks, will be taught on the pre-processed information to give predictive models for surgical decision-making, surgical planning, and postoperative care. The terminal system will be made to integrate with current robotic surgical systems, allowing for seamless integration into clinical workflows.
3.3. Discussion
The AI-assisted surgical plan can enhance surgical outcomes by providing accurate and personalized guidance to surgeons during surgery. The system can reduce surgical errors and complications, leading to faster recovery and improved patient outcomes. Nonetheless, we also have capacity limits and disadvantages that should be worked on, like the need for extensive training and technical skills to operate the system, capable biases in the algorithms learning machine, and the requirement for robust data privacy and security protocols.
To deal with these challenges, the AI-assisted surgical plan will focus on user-friendliness, with intuitive interfaces, recent updates, and alarms for capable damages and complications. Surgeons will be provided with extensive training and support to ensure the necessary technical skills are in place to operate the system effectively. The algorithm learning machine will be made to reduce biases, with routine updates and validation to ensure accuracy and fairness. Lastly, the plan will focus on information privacy and security, with the best safeguards and follow-ups in place to ensure compliance with regulations such as HIPAA.
4. Conclusion
4.1. Summary of the work done
The research examined the capability of using AI in superb medical equipment, specifically an AI-assisted surgical system. We did a comprehensive examination of the original project. We found the capability use of cases for AI in robotic surgery systems, like preoperative planning and surgical simulation, real-time intraoperative guidance, automated suturing, and postoperative monitoring and recovery management. We proposed a solution for an AI-assisted surgical plan facilitated by an algorithm learning machine that can help surgeons generate precise and data-driven decisions during surgery. We discussed the merits and demerits of the suggested solution and the stages for executing the final technology.
4.2. Future work
In the coming times, we can improve on the research and conduct to refine the suggested solution and work on capable limits and disadvantages. This comprises the improvement of the correctness and fairness of the algorithm learning machine, integrating the plan with more surgical instruments and apparatus, and addressing data privacy and security matters. To add, the forthcoming work can majorly examine the properness and effects of the AI-assisted surgical plan in clinical setups, such as calculating surgical results and overviewing the plan’s prices. Generally, the suggested AI-assisted surgical system can transform the lifecare industry and improve surgical results, and further research and development in this area are warranted.
5. References
Artificial Intelligence: A Modern Approach (3rd/4th Edition) by Stuart J. Russell and Peter Norvig
H. Haick and al, “Artificial Intelligence in Medical Sensors for Clinical Decisions” ACS Nano 2021,15, 3, 3557–3567, February 23, 2021.
Bramhe S, Pathak SS. “Robotic Surgery: A Narrative Review”. Cureus. 2022 Sep
15;14(9):e29179. doi: 10.7759/cureus.29179. PMID: 36258968; PMCID: PMC9573327.
Artificial Intelligence
Introduction
Humans have been trying to create machines that can duplicate human cognitive capacities for ages. The advancement of artificial intelligence has made this ideal a reality in the current day (AI). Can artificial intelligence be trusted in its capacity to duplicate and even outperform human intellect, given the technology’s fast advancement? This article claims that if we don’t regulate AI, ultimately, humanity will be controlled by it. Artificial intelligence (AI) development has significantly changed the technology sector and our daily lives. AI can significantly advance technology, from autonomous automobiles to automated checkouts at grocery stores (Dick, 2019). AI can automate operations, but it may also automate decision-making, which implies that people may no longer be in charge of the technology they have developed. We must exert more control over AI development to prevent it from taking over our lives.
History, Problem, Position, and Solution
The development of artificial intelligence has a long history. Researchers like Charles Babbage began looking at data processing and machine learning possibilities as early as the 19th century. With the creation of Turing Tests to assess a machine’s capacity for thought, artificial intelligence (AI) emerged in the 1950s. Since then, the sophistication and capabilities of AI have increased enormously. AI researchers worked to boost the capabilities of computers and create more complex AI systems as computing power continued to rise. Deep learning’s advancement in the 2000s accelerated AI research and paved the way for creating the first genuine AI systems. As AI technology advances and becomes more widely available, autonomous decision-making systems have been made possible thanks to the growth in AI capabilities. The applications for AI are virtually limitless; they range from self-driving cars to medical diagnostics, face recognition, and prognosis. The use of AI technology has grown significantly over the last ten years, and the International Data Corporation predicts that by 2030, the industry will be worth over $135 billion. But given this growth’s rapidity, many people are concerned about AI’s risks (Zhang & Lu, 2021).
Yet the ability of AI to regulate itself has surpassed that of humanity, which is a worry. We are vulnerable to the possibility of an AI takeover because AI systems are incredibly complicated, impossible to regulate, and anticipate with any degree of precision. In other words, AI might wield unparalleled influence over humans by duplicating and exceeding human potential. The possibility that AI will rule over humanity is a significant worry. Questions concerning who has ultimate control over AI technology’s decisions have been raised due to its ability to make judgments without human participation. AI is becoming more prevalent daily and accessible to the general population. The spread of AI research and use in the business sphere has also meant that AI-driven judgments are impacting firms’ and organizations’ decisions more and more. This makes it simple to understand how AI may someday take control of our life if appropriate legislation and monitoring are not put in place (Chen et al., 2020).
This article states that we must manage and govern AI to safeguard human civilization. Ensuring AI systems are subject to strict ethical scrutiny is the first step. We must ensure that AI algorithms and programs are created with human values and interests. To hold AI accountable for any harm it may cause, we must also create mechanisms that preserve some human control. Much discussion has concerned the risks associated with AI systems replacing humans as decision-makers. AI, a type of automation, is being used in various decision-making processes, including credit scoring and sorting job applications. This development has sparked concerns that people would no longer influence the decisions that affect our lives. Thus, requests for regulation and monitoring have grown due to the possibility of AI ruling humans. Experts in AI have suggested several legislations to guard against the possibility of AI taking over human affairs. They include laws mandating AI systems to disclose how they make decisions and putting in place procedures that allow people to step in when an AI system makes a choice that is considered too dangerous or harmful to society (Sousa et al., 2021).
This is a critical issue that has to be addressed since automated decision-making of this kind may result in unfavorable effects like discrimination or job loss. We must put in place mechanisms that allow for human control, judgment, and transparency in decision-making, in addition to being designed to be responsible and transparent to prevent such hazards. Moreover, we must be aware of the possible adverse effects of AI, such as data breaches and privacy abuses, and act to solve them. This might entail creating robust data protection systems and implementing AI-specific security measures to lessen the possibility of harmful data usage. Adopting AI ethical guidelines, such as those provided by the AI Safety Framework, must be prioritized.
Organization, Structure, and Transition
AI is a tremendous technology with the potential to change how we work and live entirely. In the meanwhile, if not adequately controlled and governed, technology also has the capability of controlling humanity. We have demonstrated the evolution of AI from its earliest stages to the present. We’ve discussed how AI may rule over people, and the precautions experts suggest to prevent that. Examples of laws and moral guidelines that may be implemented to safeguard human autonomy have been given. The development of AI has given rise to several essential worries that need to be addressed. First, it’s critical to acknowledge the possibility of excluding people from the decision-making process. Second, designing systems with ongoing human monitoring and control is crucial. Ultimately, these mechanisms need to be open and responsible so that any unfavorable results may be found and corrected. This essay’s framework is divided into three main components. The history of artificial intelligence’s development and the issues brought on by its growing sophistication are covered in the first part. The stance taken in this article that humans must act to govern and regulate AI to safeguard humanity is evaluated in the second half. The final section goes through the actions that may be made to create a successful AI regulatory framework.
Evidence Supporting Discussion
The primary source of support for this essay’s arguments is scientific analysis found in various places, including publications from AI professionals and academic journals. The claim that AI progressively eclipses humanity’s capacity to manage and control it is supported by these sources, which offer realistic instances and supporting data. Explainable AI is one technique to ensure that humans continue to be in charge of AI (XAI). A type of artificial intelligence called XAI was created to help people comprehend how decisions are made since it can describe them in simple terms. We can ensure that AI is always open and responsible by deploying XAI.
To ensure the ethical and responsible use of AI, other efforts, such as the ethical frameworks created by the High-Level Expert Group on AI of the European Commission or the Global AI Policy Observer, are also in existence. These programs are crucial for ensuring that AI is developed and applied in a way that benefits society. The media is another source of proof. They are anecdotal illustrations of the possible adverse effects that AI may have, including data breaches or privacy abuses. These instances might demonstrate the necessity of strong AI legislation to safeguard people from possible dangers (Chen et al., 2008).
Point of View
AI can advance society in unique ways, but if we are vigilant, we can reach a point when AI overrides human beings. We must thus take steps to ensure that humans continue to be in charge of AI decision-making. In addition to implementing policies to promote AI’s responsible and ethical use, we must ensure that AI systems are open and accountable.
Conclusion
AI is a compelling and quickly developing technology. The potential to engulf humanity and exercise unheard-of power over us is genuine. We must act to manage and regulate AI to assure the safety of humans and their responsible use. This article has suggested several actions that may be taken to achieve this goal, including funding research and education, setting moral guidelines, and implementing effective data protection mechanisms. If we don’t, humans may end up being controlled by AI rather than the other way around. While the promise of AI growth is intriguing, it must be handled carefully. We must take steps to guarantee that AI is utilized responsibly and ethically and that it remains under human control. We can make sure that AI makes an excellent contribution to society by implementing these policies.
References
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
Chen, S. H., Jakeman, A. J., & Norton, J. P. (2008). Artificial intelligence techniques: an introduction to their use for modeling environmental systems. Mathematics and computers in simulation, 78(2-3), 379-400.
Dick, S. (2019). Artificial intelligence.
Sousa, M., & Silva, M. (2021). Solitaire paper automation: When solitaire modern board game modes approach artificial intelligence. In 22nd International Conference on Intelligent Games and Simulation, GAME-ON 2021 (pp. 35-42).
Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and prospects. Journal of Industrial Information Integration, 23, 100224.
Future Impact of Artificial Intelligence on Work, Inequality, and Personal Liberty
Artificial Intelligence refers to the artifacts that are used to detect contexts or to implement actions in response to the identified contexts (Bryson, 2019, p. 1). Technological advancement has increased people’s ability to build AI tools and artifacts. These artifacts have affected the availability of jobs and employment opportunities positively and negatively. The current future of AI is unclear and rapidly changing with time. Artificial intelligence greatly impacts work, leads to inequality, and affects people’s personal liberty and privacy. This essay will explore the future impact of AI on work, inequality, and personal privacy and liberty.
We are currently unable to accurately forecast the future of AI for workers as there is a lack of data available. The main purpose of AI is to automate manual labor and make it easier and more time efficient. There is a general assumption that AI will make people redundant by creating super-intelligence machines that will change the course of life. According to Acemoglu (2021, p. 1), AI is likely to make advancements without paying attention to the possible effects of such growth on the essence of society such as undermining individual freedoms and democracy. This direction will affect the future of jobs as AI advances will replace workers without creating new job opportunities. Although these consequences are inevitable, the direction of AI is not preordained, but it is quite large and unexplored. The road taken by AI can be altered to create more jobs, increase productivity and shared prosperity, and bolster democratic freedoms. (As Acemoglu, 2021, p. 1), argues AI is not likely to make people redundant or create super-intelligent machines that will replace humanity. What is certain however is that AI will revolutionize various aspects of life such as entertainment, healthcare, transport, and the employment sector by enabling faster production of various tools and products. AI will also increase the amount of information that companies and the government has about people (Acemoglu, 2021, p. 1). Even though such predictions are inevitable in the world of AI, there is plenty of possibilities for AI and endless ways it can improve the lives of people. Instead of causing people to become redundant, AI will increase human productivity and efficiency and offer improved approaches to create new tasks for employees, thus ensuring that their employment positions are secured. It is possible to change the course of AI by directing research towards a productive path and changing the priorities of AI researchers to focus on aspects that progress society. For centuries now, technology has been considered a major cause of employee displacement and income inequality. It is believed to increase operational efficiency which leads to the displacement of people by machines. Technology may also lead to inequality as it may cause a few technical innovators to thrive at the expense of other innovators who get overcome by costs or lag behind in terms of technological development (Bryson, 2019, p. 6). On a positive note, technologies have disrupted lives, families, and communities for the better in the past two decades. For instance, infant mortality has reduced, and lifespans are now longer than before, which has increased general satisfaction in humans. However, if societies can reduce or resolve inequality, then problems associated with unemployment will also diminish as there will be more money in circulation. There will be a trickling effect of development associated with technology which will provide people with employment in various sectors of the economy. Technological progress since the industrial revolution has led to fear of diminished demand for labor. Although automation has made old jobs redundant, it has proven its efficiency through the creation of new ones. Although this displaced unskilled workers, AI development led to the creation of new jobs that resulted in economic progress as such IT increases production per minute resulting in general growth in the economy. In order to understand the effect of IT in the labor market, (Korinek and Stiglitz, 2021, p. 2) advise that one should analyze the effect of technology on the labor market by checking its ability to increase or decrease the demand for labor in particular wages and prices. The analysis shows that since the industrial revolution, technological progress has increased the demand for labor leading to increased material wealth and average wages in the advanced nations. Rather than replacing labor with super-intelligent machines, AI has increased the production per hour leading to heightened productivity of workers.
AI has increasingly made many aspects of our society unequal which include work, personal liberty, and privacy. This is due to technology being developed too fast, and the main focus of innovation was strictly focusing on automation which caused less demand for people in the work environment. Automation is a tool of inequality. AI has introduced new technologies that automated routine tasks that are initially done by unskilled workers in factories and farms. This led to causing the wages and demand for such workers in common occupations and clerical positions to decline. On the contrary, the demand for professionals in finance, managerial, design, consulting, and engineering increased as they were considered essential to the success of the new innovations. This group of people benefited from higher wages and high demand (Acemoglu, 2021, p. 4). This widened the wage gap between skilled and unskilled workers, resulting in inequality in society. AI has led to wealth inequality and employment disruption by shielding wealthy individuals and companies from liability at the expense of the vulnerable in society. In order to address the challenges caused by automation and technological advancement, there is a need to shift the focus from the taxation of income to the documentation and taxation of wealth. However, this will require successful redistribution both nationally and internationally as most of the wealthiest organizations derive their wealth from the global space (Bryson, 2019, p. 11). This approach will reduce inequality, which will cause problems associated with employment to diminish significantly. The inequality caused by AI has been exacerbated by the availability of a wide range of cobots and chatbots. Most particularly, cobots used in factories that assemble cars and manufacture car parts have replaced a range of workers (Moore, 2021, p. 7). Automation has replaced the low-skilled work done by humans with robots that are augmented with autonomous machine behavior. Such automation has replaced workers in terms of their brains and limbs, as the machines are built to think and work like people.
During the rise of AI, people have become more worried about the fact that AI could be an invasion of their privacy. This is due to businesses using personal data to feed us information and products that we have searched for on our personal devices previously. This could be very irritating to internet users as product advertisements would keep coming up on every social media platform that they may use over and over until they feel obligated to buy it. AI affects personal privacy and liberty. Automation causes people to become easy to manipulate and predict, which further exposes them to attack and oppression from those who do not believe in them. It affects one’s liberty as it may expose one to threats from the state or other powerful parties in society. Such restrictions on personal liberty and inhibited free expression limits one’s ability to innovate, which slows society’s growth as a whole (Bryson, 2019, p. 7). Another risk associated with automation is that it increases the risk of losing privacy as a result of complex algorithmic systems (Murdoch, 2021, p. 3). This may result in data breaches in various sectors of the economy increasing the overall vulnerability of individuals and organizations. Going forward, businesses have a duty to adopt AI to increase productivity. As explained by Schmitz, businesses require to use AI as a tool and acquire more data to increase efficiency (Schmitz, 2022, p. 2). While increasing the amount of data, it is also important to ensure transparency about the use of the collected data and how the data will be used. The business should have a legal and legitimate basis to use the collected data in order to promote the privacy of data and eliminate challenges associated with large amounts of data (Murdoch, 2021, p. 3). Increasing efficiency in the collection of data and improving the automation processes such as management of customer relations will benefit the business in the long run.
In conclusion, my analysis of the texts shows that artificial intelligence affects work, leads to inequality, and affects people’s liberty and privacy. Artificial intelligence has led to the creation of tools that have increased operational efficiency in the workplace. Increased productivity has resulted in the loss of manpower as machineries such as cobots and chatbots are considered more efficient, replacing people. AI has also led to the loss of privacy and personal liberty. Businesses investing in AI should implement measures that promote the privacy of data and protect people from possible harass and abuse by third parties.
References
Acemoglu, D. (2021). AI’s Future Doesn’t Have to Be Dystopian. Boston Review, May, 20, p.2021.
Bryson, J.J. (2019). The past decade and future of AI’s impact on society. Towards a new enlightenment, pp.150-185.
Korinek, A. and Stiglitz, J.E. (2021). Covid-19 driven advances in automation and artificial intelligence risk exacerbating economic inequality. bmj, 372.
Moore, P. V. (2019). Artificial Intelligence in the Workplace: What is at Stake for Workers? In Work in the Age of Data. Madrid: BBVA, 2019.
Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22(1), pp.1-5.
Schmitz, M. (2022). Artificial intelligence and data privacy. Usercentrics, April 12, 2022
Artificial Intelligence in the Workplace
While robots are yet to roam in office hallways, taking over human jobs, the reality of the artificial intelligence system has already infiltrated most of the United States’ workplaces. In the near future, AIs and robots will increasingly perform most of the routine jobs held by humans. Humans will handle only the more complicated tasks because of their bigger perspective and interpersonal skills. This technological progress offers both peril and promises as it revolutionizes the future workplace, personal lives, and the economy. There is hope for advances in productivity, health, and safety, but significant economic disruptions, especially in the workforce, are inevitable (Plastino and Purdy, 2018). On one side, artificial intelligence and robotics will benefit the workplace by saving coworkers from having to do repetitive and often tedious tasks that are part of their job description. On the other side, the concern is that even though this technology enhances efficiency and productivity, pay and benefits will be affected because people may have to spend less time at the workplace (Buchmeister, Palcic, and Ojstersek, 2019). Additionally, people with low-level education who primarily perform repetitive jobs may lose their source of employment and source of income. Others argue that AI and robotics will create more jobs than they eliminate. This paper will provide a general overview of the increased adoption of robots and artificial intelligence in the workplace.
Despite the significant boost in productivity and efficiency at the workplace, there is a challenge in implementing the technology into human resource procedures and practices. Combined with other progressive technologies such as 3D printing, robotics, and artificial intelligence, it will bring more efficiency in producing goods and services (Plastino and Purdy, 2018). AI systems already perform repetitive tasks but can also be trained to undertake a wide range of cognitive and non-routine functions. For instance, advanced robotics are increasingly able to carry out manual tasks. The workplace and society as a whole will benefit from lower production costs and improved productivity, but many employees will be negatively affected. According to recent research, 50 percent of today’s working population is in sectors vulnerable to such disruption in the near future. For instance, some automakers are rolling out autonomous trucks, which will replace thousands of drivers. Recent reports also predict that machines could replace over 2 million more workers in the manufacturing industry in the next three years (Buchmeister, Palcic, and Ojstersek, 2019). Even middle-level jobs that require cognition are vulnerable to this shift in workplace technology. Some of the jobs that artificial intelligence can automate include loan underwriting, tax preparation, radiology, financial analysis, and software engineering.
Another study found that 47 percent of white-collar jobs are at high risk of automation over the next ten years (Chelliah, 2017). For example, since the introduction of IBM’s Watson technology in diagnosis, many healthcare employees that previously performed diagnostic tasks have been rendered redundant. Entry-level tasks for lawyers are being automated, such as AI and robots scanning thousands of precedents and legal briefs in seconds to facilitate pre-trial research. Artificial intelligence has introduced sophisticated algorithms that are gradually replacing humans in several tasks previously performed by patent and contract lawyers and paralegals. The automation and computerization of tasks are made possible by the availability of big data. Specifically, the invention and advancement in sensing technology have made it possible to collect big data through sensor data. Another labor-intensive industry that is about to be disrupted by AI and robotics is education (Bhargava, Bester, and Bolton, 2021). In recent years, there has been a huge rise in open online classes and courses that generate large data sets with details of students’ interaction on education forums, grades, and their aptitude in completing assignments and covering lectures (Chelliah, 2017). The most likely white-collar jobs to be taken over by robotics and AI due to increased machine learning include civil engineers and technicians, surveyors, market research specialists and analysts, technical writers, accountants and auditors, and examiners.
Artificial intelligence and robotics have already disrupted the workplace by replacing humans with tasks that can be automated, especially in production. Technology is gradually replacing service jobs, particularly those involving customer engagement. Adopting this innovation in the service sector will lead to job losses, and customers will lose the chance to obtain human service (Huang and Rust, 2018). Artificial intelligence and robotics technology in the service sector involves four intelligences: mechanical, analytical, intuitive, and empathetic. Mechanical intelligence involves the capacity to automatically perform repeated, routine tasks. Humans provide unskilled mechanical labor in the service sector without necessarily undergoing advanced education or training (Bhargava, Bester, and Bolton, 2021). Some service employees who only require mechanical skills are call center agents, customer care handlers, and waiters and waitresses. Their tasks involve processes that have been performed numerous times and thus do not require much thinking or creativity (Brougham and Haar, 2018). Analytical intelligence concerns with the capacity to process information to solve problems and learn in the process. Analytical skills are used by engineers, data scientists, accountants, financial analysts, and technology-related employees. AI and robotics perform their roles through training, machine learning, specialization in cognitive thinking, and expertise.
Intuitive intelligence also threatens job security for many professionals because it involves the ability of machines and systems to think creatively and adjust accordingly depending on the situation. Huang and Rust (2018) consider it a wisdom-based experience and holistic thinking. Robots and AIs will likely take over jobs requiring hard thinking and advanced professional skills requiring creative problem-solving and insights. Such jobs include lawyers, marketing managers, doctors, management consultants, and senior travel agents. Artificial intelligence is increasingly used in these roles because it is improbable for AI systems or robotics to commit a mistake twice due to their learning nature (Bhargava, Bester, and Bolton, 2021). What prevents AI from being widely implemented in the workplace for such jobs is that humans provide consciousness, self-awareness, and sentience. However, with machine learning technology, AI is acquiring those attributes. Humans also have the upper hand over machines and systems because of their empathetic intelligence (Brougham and Haar, 2018). It is the capacity to recognize and interpret others’ emotions, react appropriately, and influence other people’s emotions. Humans have interpersonal skills that enable them to work well with others by being sensitive to their feelings. Currently, there are AI that can recognize how users feel and behave as if to have feelings. Empathetic AI, such as social media technologies, recognizes the crucial role of emotions in human cognition and perception (Huang and Rust, 2018). For example, Sophia is a sophisticated robot designed to appear and behave like humans. Such technology will replace people working in the service sector or providing psychological comfort, babysitting, and well-being in nursing homes.
Apart from rendering many workers redundant, robots and artificial intelligence will significantly change the type of employment for ordinary U.S. workers. First, technology can reduce the range of tasks humans perform but cannot entirely replace them in some jobs (Plastino and Purdy, 2018). However, they will be left with fewer duties, which could mean fewer working hours and/or static or reduced wages. Companies will strive to lower their expenses by having more part-time and remote employees. Artificial intelligence has made it possible for some workers to perform their tasks from the comfort of their homes because machines and systems can perform roles that require physical presence. This will affect their remuneration as well as their employment contracts. Moreover, the integration of artificial intelligence in HR practices will also affect employment conditions (Brougham and Haar, 2018). For instance, workplaces can embed AI in their HR procedures by setting predictive algorithms that can undertake recruitment and performance appraisal tasks. The algorithmic tools can analyze resumes, predict job performance, and sometimes perform facial analysis during interviews to assess the interviewee’s attention span and optimism.
Artificial intelligence and robotics use in human resource practices, such as supervised machine learning, will streamline HR processes in the workplace by enabling fairness in employment processes. Technology has the potential to avoid subjectivity and biases inherent in the human subconscious. However, it will equally affect some employees because of its capacity to replicate human subjectivity and biases and enable systematic discrimination (Plastino and Purdy, 2018). The AI algorithms are informed by historical data during their machine learning. The historical data is not free of human biases or systematic discrimination. Using that data to predict a candidate’s suitability, for instance, will replicate those vices in the recruitment process. Also, most of these algorithms are trained to recognize word patterns in resumes rather than skill sets to assess the candidate’s suitability for the job. The algorithm is designed to streamline the hiring process by identifying applicants whose resumes have relatively similar attributes to those of historically successful employees (Brougham and Haar, 2018). The similarity indicates the likelihood of these candidates performing well for the company. Therefore, AIs and robots can replace human tasks in HR processes but will not necessarily address the issue of biases and subjectivity.
The paper has provided a general overview of the increased adoption of robots and artificial intelligence in the workplace. AI systems already perform repetitive tasks but can also be trained to undertake various cognitive and non-routine functions. Even middle-level jobs that require cognition are vulnerable to this shift in workplace technology. The workplace and society as a whole will benefit from lower production costs and improved productivity, but many employees will be negatively affected. Research shows that half of U.S. workers are in sectors vulnerable to such disruption in the near future. Artificial intelligence has introduced sophisticated algorithms that are gradually replacing humans in several tasks previously performed by white-collar employees. Technology is also replacing service jobs, particularly those that involve customer engagement. Adopting this innovation in the service sector will lead to job losses, and customers will lose the chance to obtain human service. Apart from rendering many workers redundant, robots and artificial intelligence will significantly change the type of employment for ordinary U.S. workers who will have fewer duties, which could mean fewer working hours and/or static or reduced wages.
References
Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106-113.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.
Buchmeister, B., Palcic, I., & Ojstersek, R. (2019). Artificial Intelligence in Manufacturing Companies And Broader: An Overview. DAAAM International Scientific Book.
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Plastino, E., & Purdy, M. (2018). Game-changing value from artificial intelligence: eight strategies. Strategy & Leadership.
The Use of Artificial Intelligence in Cancer Diagnosis and Treatment
Introduction
Building machines capable of carrying out activities that need human intelligence falls under the broad umbrella of artificial intelligence, a branch of computer science. It often refers to computational innovations that mimic human intelligence-assisted processes like cognition, engagement, deep learning, sensory perception, and adaptation. Some machines can do tasks that usually require human interpretation and judgment calls. AI and similar technologies are becoming more and more common in society and business and are starting to be used in healthcare. These technologies could transform many facets of patient care and operational procedures within providers, payers, and pharmaceutical companies. Researchers and healthcare professionals are paying attention to artificial intelligence (AI) in the healthcare industry. Few prior research, including those in business, accounting, management, decision sciences, and the health professions, have examined this subject from a multidisciplinary angle. The application of AI in healthcare is expected to increase significantly. Gene editing, medication research, personalized medicine, supportive healthcare services, and illness diagnosis are some of the current applications. Diagnosis, discovery, and treatment planning have all been revolutionized by artificial intelligence (AI). In addition to helping with cancer detection, it can also help with cancer therapy design, finding novel therapeutic targets by speeding up drug discovery, and enhancing cancer surveillance by looking at patient and cancer statistics. AI-guided cancer treatment may improve clinical management and screening with more positive health outcomes. This thorough study offers a systematic assessment of the literature on using artificial intelligence (AI) in cancer diagnosis and provides crucial insights into the uses of AI for cancer treatment.
Background
There are currently several cancer treatment options. Since the 2010s, cancer treatment has become significantly more effective. Despite the profusion of modern instruments, precise diagnostic therapies that are scientifically effective for each sick person are still challenging. Therefore, a patient-specific, optimal treatment may be used if an accurate diagnosis could be made. Improved forecast accuracy could aid doctors in better planning patient therapies and reducing the pain and suffering brought on by the condition. After treatment, cancer can return fast, and it can be challenging to detect in the early stages. Furthermore, making exact predictions of clinical diagnoses is exceedingly tricky. Some cancers are difficult to see in the early stages due to their vague symptoms and the hard-to-distinguish telltale indications on mammograms and scans. Therefore, developing better predictive models using multivariate data and cutting-edge diagnostic technology for clinical cancer research is essential. According to a quick literature study, AI is more precise than traditional analytical methods like data analysis and multivariate analysis. This is particularly true when cutting-edge bioinformatics tools are used with AI because this can significantly improve the precision of prognostic, diagnostic, and predictive techniques. A more specific idea known as machine learning (ML) is becoming more popular. To predict a patient’s chances of survival, prediction models are developed using machine learning (ML), a subset of artificial intelligence. Machine Learning learns logical patterns from a significant amount of historical information.
Research Question
How is artificial intelligence applied in cancer diagnosis and treatment?
Review Process
This review is drafted using contextual investigation examination of different and heterogeneous sources mainly academic journals, periodicals, websites and websites. The review permits a comprehensive understanding of the application of artificial intelligence in cancer treatment and diagnosis.
Literature Review
Artificial Intelligence in Examining Tissue Changes
Malignant tumors are incredibly aggressive and endanger the lives of those they affect because of the fast cell multiplication, invasion of adjacent organs, and formation of new growths known as metastases. Tumors are now categorized using a variety of methods. For instance, experts can use medical imaging to remove Tissue and then study it under a microscope to determine how different healthy Tissue and malignant tissues can be distinguished (Huang et al.)[1]. Important information can be gleaned from the primary tumor’s size and location as well as the presence of distant metastases. Tumor markers can also be utilized in laboratory diagnostic procedures to demonstrate the existence of a tumor as per (Searching for the Causes of Cancer)[2]. Artificial intelligence-based picture identification technology has advanced significantly in recent years. Numerous researchers and businesses are now developing various approaches to increase the efficiency, precision, and affordability of cancer screening. While a few of these are just beginning, others are further along, like Paige. AI produces software for prostate and breast cancer that has the CE label. AI can serve as a second set of eyes for pathologists, enabling them to discover cancerous growths on a specimen they might otherwise miss. Better accuracy and overall improved patient insights may result from this. In environments with limited resources, AI can be beneficial for cancer screening. Compared to radiologists, an AI program can learn from a more comprehensive library.
Use of AI in the Prediction of Cancerous Genetic Mutations
Interest in AI applications in these crucial fields has grown over the past few years, sometimes with performance on par with human specialists and benefits in scalability and time-saving (Chen et al., 2021)[3]. According to (Chen et al.) the underlying epigenetic and genetic heterogeneity has also been described using histopathology pictures and Deep Learning techniques. Additionally, the authors argue that Deep Learning techniques predicted whole-genome duplications, chromosomal arm losses, gains in focal additions and deletions, and gene changes for pan-cancer have all been foretold. In addition to predicting mutations in specific genes, Deep Learning models have been used to forecast mutational footprints, which are the most significant biomarkers for responses to checkpoint immunotherapy. Examples of these mutational footprints include microsatellite instability (MSI) status and tumor mutational burden (TMB) status.
Artificial Intelligence in Drug Discovery
(Boniolo et al.) insists that one of the best instances of how AI has been utilized to produce tailored medicinal ingredients is personalized cancer vaccinations. Cancer vaccines need to identify antigen peptides that are extremely specific to the patient’s tumor and MHC genotype to strengthen the patient’s immune system. According to (Boniolo et al.)[4] most individualized vaccine design processes now incorporate optimization techniques and machine learning to help with peptide identification and vaccine assembly. Such vaccine design frameworks are advantageous for population-level preventive vaccine development against contagious diseases because they allow for selecting a target antigen and set of MHC alleles, making them amenable to customized cancer immunotherapy. Similarly, AI-driven medicine discovery for both large and small molecules has recently experienced results, with some examples of tailored applications.
Cancer immunotherapies use the immune system of the patient to combat malignancies. Unique genetic changes that give rise to neoepitopes, a class of self-peptides linked to the primary histocompatibility complex (MHC) and used to distinguish between cancerous and healthy cells, may occur during the evolution of tumors. Since then, AI has emerged as a crucial component of many cancer vaccine design pipelines, from predicting neoepitopes from a patient’s distinctively altered peptide pool to selecting and assembling the neoepitopes into vaccines. AI-based methods for personalized vaccine design have increasingly developed such cancer-specific peptides as per (Boniolo et al., 2021).
The use of AI in medicine development begins with AI is utilized at every stage of the current pipeline for cancer vaccines. The final vaccination is chosen and put together using constraint optimization models, which are used to find cancer-specific antigenic peptides. In recent years, generative models that use co-evolutionary data from protein sequences have changed structural biology. These models are currently used to predict 3D structures, speed up molecular dynamics simulations, and create new proteins. Deep neural networks are now revolutionizing the creation of new tiny compounds. With generative models, it is simple to develop novel molecules with enhanced biological properties while exploring a sizable percentage of the chemical search space (Boniolo et al., 2021).
AI is used to predict the effects of anticancer medications or to facilitate the discovery of anticancer treatments. Different cancers and drugs might react differently, and data from extensive screening procedures frequently shows a connection between the genetic variety of cancer cells and therapeutic efficacy. Researchers produced fake data using monitoring data and ML. The methodology is used to forecast the effectiveness of anticancer drugs based on the location of the present mutation in a malignant cell’s genome (Alqahtani, 2022)[5]. Moreover, AI holds great promise for determining an anticancer drug’s susceptibility (Alqahtani, 2022). AI plays a crucial role in fighting against cancer drug resistance. AI can quickly understand how cancer cells develop immunity to cancer treatments by analyzing data on large drug-resistant tumors, which can help advance drug development and usage.
Artificial Intelligence in Cancer Therapy
The potential impact of artificial intelligence (AI) methods on several aspects of cancer therapy is extensive. These include the creation and development of pharmaceuticals and the clinical validation and eventual administration of these drugs at the point of care, among other things(Ho, 2020)[6]. At the moment, these procedures are costly and time-consuming. Furthermore, different patients experience different effects from their therapy. There are numerous strategies to deal with these issues due to the convergence of AI and cancer therapy. Machine learning and neural networks are the only AI platforms that can speed up drug discovery, use biomarkers to precisely match patients to clinical trials, and truly customize cancer treatment using only the patient’s data (Ho, 2020). Some medicines stimulate the body’s defense mechanisms to fight cancer cells, as with immunotherapy. The cancer cells themselves, however, target the three main pillars of modern cancer therapy: surgical removal of the tumor, chemotherapy, and radiotherapy. Treating cancer using radiotherapy involves using ionizing or particle radiation to damage the genetic material of cells to prevent them from dividing further (Iqbal et al., 2021)[7]. This has proven to be one of the most effective methods for shrinking or eliminating tumors since the 20th century. Although the radiation also damages healthy cells, these cells are unlike their diseased counterparts in that they are better able to repair themselves – depending on the severity of the damage. Whereas the cancer cells die off, the plan is for the healthy tissue to regenerate. For this reason, historically, radiation dose has been administered over several sessions, known as fractions—to give the healthy tissues time to repair themselves between treatment sessions. However, recent advances in precision that enable targeted treatments that better protect the surrounding healthy tissues have enabled more precise radiotherapy treatments, such as intensity-modulated and image-guided radiation therapy.
Barriers to adopting AI in Cancer Treatment
Lack of organized cancer-related health data and a lack of uniformity in the collection and storage of unstructured data inside an EHR or integrated database of a given healthcare system present significant challenges for the database creation of AI models (Alkhaldi, 2021)[8]. Because it restricts interoperability and the mass exchange of health information and data, the absence of uniformity across healthcare systems and governments worldwide is even more significant. To tackle this, the Minimum Common Oncology Data Elements program created standardized terminology and descriptions for the frequently used patient- and tumor-related features, disease status categories, and treatment interventions. Its use in ordinary clinical practice is still being investigated, and its application necessitates critical information technologies and systems resources.
Additionally, the “black box” aspect of the mechanism, which is frequently discussed, makes it difficult for medical professionals to accept AI technologies. This is especially true for systems based on deep learning and neural networks, which depend on complex hidden neurons of data interaction. Although simple ML algorithms, such as linear regression, operate perfectly transparently, many contemporary approaches use techniques that involve creating many overlaying decision trees with complex reinforcement schemes that cannot be usefully depicted graphically. DL, which is based on hidden layers of data interaction inspired by the interconnection of the neurons and synapses of the brain, further complicates interpretability (Shreve et al.)[9].
Low levels of expertise cause a challenge in understanding an AI-powered study hence becoming another roadblock to the adoption of AI in healthcare (Victor Mugabe)[10]. Instead of focusing on how the science is technically carried out, principal investigators should discover what topics AI is particularly well-suited to address. AI tries to model a complicated system and provide precise predictions, in contrast to traditional statistical methods used to evaluate correlations between variables and provide directed hypothesis testing. For widespread acceptance, there must be open communication between healthcare experts, the public, and developers (Albert, 2021)[11].
Results
Uses of Artificial Intelligence in Cancer Screening
The most important factors influencing treatment choices and patient outcomes are cancer screening for timely detection, precise cancer diagnosis, classification, and grading. AI could be used for cancer screening in a variety of ways, including pre-screening to weed out patients with low cancer risk, replacing radiologists as readers entirely, replacing one radiologist in a configuration with two or more readers, assisting radiologists in making diagnoses, and adding an extra level of diagnostic testing upon basic radiology assessment. Each of these calls for a slightly different strategy for developing and testing AI technologies. More research and development are required before AI-based diagnostics are used widely in cancer testing and other medical fields.
Uses of Artificial Intelligence in Cancer Treatment
For many years, surgery, chemotherapy, and radiotherapy will still be the conventional cancer treatments, but the scientific community is becoming increasingly interested in developing the current clinical cancer treatment approaches. According to research, after systematically analyzing data from large pharmaceutical and clinical datasets, AI is recognized as one of the top futuristic therapies for accurate cancer diagnosis, prognosis, and treatment. The application of algorithm-based AI support for radiology image processing, data mining, and electronic health records, to give a more precise answer for cancer therapy is anticipated to alter clinical practices and digital healthcare in the future. The impact of ML on healthcare procedures is significant. It may impact diagnosis and therapy, raising important ethical questions. Applications for machine learning in healthcare span from completely autonomous AI for cancer diagnosis to nonlinear mortality estimates to help with resource allocation.
Discussion
The prediction of cancer can benefit from AI and machine learning. Artificial intelligence can spot malignancies that have already spread and people at a high risk of getting it before it does. Medical professionals monitor these patients closely and act quickly when necessary. Additionally, ML in cancer detection can assist people in receiving preliminary feedback on their anomalies through self-diagnosis without needing a doctor’s appointment. Such tools do not always result in a conclusive diagnosis. The approval of pathologists is required. The same medicine may respond differently to various forms of cancer. AI can forecast how different drugs would affect malignant cells. This information aids in the creation of new anticancer medications and the timing of their use. Creating individualized treatments and tumor characterization can benefit from improving genome sequencing with artificial intelligence.
The detection and treatment of cancer can significantly benefit from artificial intelligence. To begin with, artificial intelligence makes room for tailored cancer therapy procedures. Big data and AI enable medical professionals to examine various data about the patient and the cancer cells to develop individualized treatments. The side effects of this kind of therapy will be less severe. Less harm will be done to healthy cells, but it will significantly affect cancer cells. Second, the use of artificial intelligence in the detection and treatment of cancer enhances diagnostic precision by lowering false-positive and false-negative results. Proof comes from studies on breast cancer detection. One in ten female patients with mammograms examined by doctors has false-positive results, forcing them to undergo stressful procedures and unnecessary invasive testing. The research team at Google created software that uses AI to reduce false positive and false negative mammography readings by 6% and 9%, respectively. Another group of researchers developed an AI algorithm to identify breast cancer. This algorithm assisted radiologists in lowering false-positive rates by 37.3% during an examination.
Additionally, the development of artificial intelligence has made it possible to identify cancers without invasive techniques. Sometimes the tumor’s benign nature is discovered only after the removal surgery, which would have allowed the procedure to be avoided entirely. Such occurrences can be significantly decreased with AI’s assistance in the cancer detection process. Image-guided needle biopsies can train machine-learning algorithms to recognize malignant tumors. Without the need for intrusive procedures that would otherwise be necessary, they can identify and characterize the isocitrate dehydrogenase (IDH) mutations from MRI imaging of gliomas. In the end, artificial intelligence is crucial in preventing cancer overtreatment by assisting radiologists in determining which tumors and anomalies are malignant and require actual treatment. Artificial intelligence (AI) systems can identify precancerous lesions in photos and separate them from other abnormalities, preventing over-treating patients for unimportant conditions.
Conclusion
Artificial intelligence has the potential to save both patients’ lives and physicians’ time. Early cancer diagnosis could be revolutionized by using AI in healthcare data, and automation could support concerns about capacity. AI may make it possible to analyze complex data from various sources, including radiomic, genomic, metabolomic, and clinical text data. It is envisaged that ongoing research to assist the application of AI to cancer genomes would enable multicancer early detection and tumor site origin determination. This may improve cancer survivors’ surveillance plans and change cancer screening, especially for less common and rare tumors. The difficulties in designing, implementing, and maintaining AI models that have been raised are significant but not insurmountable. Initial AI tools now becoming accessible within the EHR are helping cancer practitioners, and there is a clamor for their potential future applications.
Works Cited
Albert, Helen. “AI for Cancer Detection: Ready for Prime Time or Caution Advised?” Inside Precision Medicine, 24 Sept. 2021, www.insideprecisionmedicine.com/artificial-intelligence/ai-for-cancer-detection-ready-for-prime-time-or-caution-advised/?gclid=Cj0KCQjw9ZGYBhCEARIsAEUXITXO-btdwuec8pxaZJdr9lU8rdK5jmrQDdf5NQeXNG4–VhTq-HlXfIaAnWlEALw_wcB.
Alkhaldi, Nadejda. “AI in Cancer Detection and Treatment: Applications, Benefits, and Challenges.” ITRex, 22 Oct. 2021, itrexgroup.com/blog/ai-in-cancer-detection-treatment-applications-benefits-challenges/#header.
Alqahtani, Amal. “Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents.” Evidence-Based Complementary and Alternative Medicine, edited by Arpita Roy, vol. 2022, Apr. 2022, pp. 1–16, https://doi.org/10.1155/2022/6201067.
Boniolo, Fabio, et al. “Artificial Intelligence in Early Drug Discovery Enabling Precision Medicine.” Expert Opinion on Drug Discovery, June 2021, pp. 1–17, https://doi.org/10.1080/17460441.2021.1918096.
Chen, Zi‐Hang, et al. “Artificial Intelligence for Assisting Cancer Diagnosis and Treatment in the Era of Precision Medicine.” Cancer Communications, vol. 41, no. 11, Oct. 2021, pp. 1100–15, https://doi.org/10.1002/cac2.12215.
Ho, Dean. “Artificial Intelligence in Cancer Therapy.” Science, vol. 367, no. 6481, Feb. 2020, pp. 982–83, https://doi.org/10.1126/science.aaz3023.
Huang, Shigao, et al. “Artificial Intelligence in Cancer Diagnosis and Prognosis: Opportunities and Challenges.” Cancer Letters, vol. 471, Dec. 2019, https://doi.org/10.1016/j.canlet.2019.12.007.
Iqbal, Muhammad Javed, et al. “Clinical Applications of Artificial Intelligence and Machine Learning in Cancer Diagnosis: Looking into the Future.” Cancer Cell International, vol. 21, no. 1, May 2021, https://doi.org/10.1186/s12935-021-01981-1.
“Searching for the Causes of Cancer.” Www.siemens-Healthineers.com, 30 July 2021, www.siemens-healthineers.com/perspectives/causes-of-cancer.
Shreve, Jacob T., et al. “Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations.” American Society of Clinical Oncology Educational Book, no. 42, July 2022, pp. 842–51, https://doi.org/10.1200/edbk_350652.
Victor Mugabe, Koki. “Barriers and Facilitators to the Adoption of Artificial Intelligence in Radiation Oncology: A New Zealand Study.” Technical Innovations & Patient Support in Radiation Oncology, vol. 18, June 2021, pp. 16–21, https://doi.org/10.1016/j.tipsro.2021.03.004.
[1] Artificial Intelligence in Cancer Diagnosis and Prognosis: Opportunities and Challenges
[2] Searching the Causes of Cancer
[3] Artificial Intelligence for Assisting Cancer Diagnosis and Treatment in the Era of Precision Medicine
[4] Artificial Intelligence in Early Drug Discovery Enabling Precision Medicine
[5] Application of Artificial Intelligence in the Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents
[6] Artificial Intelligence in Cancer Therapy
[7] Clinical Applications of Artificial Intelligence and Machine Learning in Cancer Diagnosis: Looking into the Future
[8] AI in Cancer Detection and Treatment: Applications, Benefits, and Challenges
[9] Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations
[10] Barriers and Facilitators to the Adoption of Artificial Intelligence in Radiation Oncology: A New Zealand Study
[11] AI for Cancer Detection: Ready for Prime Time or Caution Advised
Artificial Intelligence Is a Threat to Humanity
Introduction
Artificial Intelligence (AI) will continue to play a crucial role in developing cutting-edge computer science and software. However, due to its fast growth and increasing incorporation into everyday life and the workplace, technology also presents hazards to society and new opportunities for people. The development and success of the European economy, for instance, depend on the cutting-edge technology employed at every production stage. On the other hand, some prominent figures—Bill Gates, who has expressed concern about AI’s(Artificial Intelligence) potential dangers to society, and Stephen Hawking, who is still actively engaged in cosmological research, among other fields—have warned against its use in the development of weapons. In this article, we’ll examine AI’s(Artificial Intelligence) pros and cons to determine whether it’s a threat or an opportunity for humanity.
Importance of Artificial Intelligence
Europe’s leading economies in internet data and its uses are located inside its technologically advanced countries. These economies rapidly climb to the top thanks to advances in Artificial Intelligence (AI). However, due to developments in IT(Artificial Intelligence), patients worldwide now have access to healthcare facilities that provide good service, and doctors and nurses have more options for treating patients and administering their treatments. As a result of advancements in this field, socially theurapic robots have been created to assist the elderly and the economically disadvantaged. Human fatigue is a common source of mistakes, but AI(Artificial Intelligence) can help mitigate this problem because it does not experience fatigue or weariness as humans do (Parekh et al.,p. 1-17).
When it comes to performing operations, artificial intelligence plays a crucial part. Moreover, the development of the da Vinci surgical system, which is essentially operated by robots and guarantees few procedures during operations, makes this conceivable. In addition, this technology enables a patient’s virtual presence in a hospital, meaning that even the most seriously sick patients who have been advised to get care at home do not have to leave their beds to do so. Because of Artificial Intelligence, procedures can be completed rapidly and with high quality in a short time (Bostrom et al., p.57-69).
The timely delivery of findings is ensured by the fact that artificial intelligence is used more often to perform arduous jobs, such as lifting cargo from ports, that humans cannot execute. The technology also aids with weather forecasting, which is helpful in planning ahead for things like planting and harvesting seasons and what to wear. This technology(Artificial Intelligence) is also beneficial in military operations since intelligence predicts when the adversary will strike, reducing the risk to human life( Johnson, p. 147). The development of this new technology necessitates the acquisition of new skills, which in turn creates new employment prospects in the form of positions for trainers. Last but not least, unlike humans, AI (Artificial Intelligence) is always on the job, as it never takes breaks to sleep, eat, or do any of the other mundane things that make up human life.
Eliminating cyber attacks, discouraging the spread of false Information, and ensuring that Information is of good quality are essential steps toward strengthening democracy. In contrast, AI promotes openness and diversity, which may be explained by the fact that analytical data is employed in place of subjective opinions throughout the recruiting process. Crimes, including terrorist acts, can be stopped in their tracks with the use of artificial intelligence because of how early warning systems can detect and prevent them. Regarding security in general, AI(Artificial Intelligence) is deployed during assault and defense. The public sector can benefit significantly from this technology since it helps reduce costs and opens up new opportunities in the areas of education, energy, and waste management.
Negative Effects of Artificial Intelligence on Humanity
Continued use of Artificial Intelligence creates inequalities in people’s access to Information because some people may have more internet access than others. Secondly, if AI(Artificial Intelligence) is used too extensively, it could cause a significant loss of employment opportunities as human tasks are automated; as a result, organizations may need to invest more money in educational and training programs to compensate for the shortage of human talent. Moreover, Individual administrations will establish new laws and regulations to ensure the safety of global interactions through AI(Artificial Intelligence), which will lead to global regulations. More hacking incidents have arisen due to this technology; this is because various operations may be executed by the technology much more quickly than by humans, which can result in the propagation of viruses. Terrorism linked to artificial intelligence could result from the widespread adoption of autonomous drones and the introduction of robotic swarms to perform formerly human-only activities, requiring security personnel to acquire new expertise to counteract the risks posed by the technology.
When AI(Artificial Intelligence) takes over manual labor, workers suddenly find themselves with more time on their hands. While this may be welcomed by many, it also raises the possibility that others will choose to utilize their newfound leisure time to participate in unlawful behavior. Another disadvantage of artificial intelligence is the loss of privacy that results from the widespread availability of related data and Information in the digital realm. The exponential growth of the internet and its users may constrain societal progress since it relies on data from the past to forecast the future, increasing the role it plays in decision-making while diminishing the importance of human values.
Because there is no education or preparation offered to educate people about artificial intelligence’s effects, its consistent use can result in life-threatening situations, which is made feasible by the lack of training. Accidents in the manufacturing industry and oil spills are two examples of life-threatening incidents that can severely impact human health. This technology uses algorithms to set and make decisions, which is a major disadvantage due to the fact that there are no emotions attached to such algorithms. The lack of emotional intelligence is a disadvantage regarding this technology(Artificial Intelligence) because of the use of such algorithms to set and make decisions (Nadikattu and Ashok Kumar Reddy, p. 2320).
To sum up, several scientists, including Stephen Hawking and Nick Bostrom, have expressed skepticism about the benefits that AI(Artificial Intelligence) could have for humanity. Among others, it lays out how technology has altered human nature and the pursuits that surround it. And this is due to the general fear of intelligent beings, whether they be human or mechanical. Despite concerns about the harmful effects of this technology on society, there has been no news to suggest that its development will be halted. The benefits of AI(Artificial Intelligence) outweigh the drawbacks, and the field should be proud of its ability to address and ultimately eliminate issues plaguing the human labor force.
Works Cited
Bostrom, Nick, and Eliezer Yudkowsky. “The ethics of artificial intelligence.” Artificial intelligence safety and security. Chapman and Hall/CRC, 2018. 57-69.
Johnson, James. “Artificial intelligence & future warfare: implications for international security.” Defense & Security Analysis 35.2 (2019): 147-169.
Nadikattu, Ashok Kumar Reddy. “Influence of Artificial Intelligence on Robotics Industry.” International Journal of Creative Research Thoughts (IJCRT), ISSN: 2320-2882.
Parekh, Vidhi, Darshan Shah, and Manan Shah. “Fatigue detection using artificial intelligence framework.” Augmented Human Research 5.1 (2020): 1-17.
Artificial Intelligence (Article Review)
Introduction
This essay’s purpose is to critically analyze an article that focuses on artificial intelligence. Specifically, the analysis will focus on some of the most critical aspects of the article, including a review of the article’s objectives, strengths and weaknesses, a conclusion, and various recommendations. The study titled “Artificial intelligence in healthcare: past, present, and future” was written by Jiang et al. (2017) and is the subject of this review. In this section, the authors discuss how artificial intelligence (AI) has recently made significant waves in the healthcare industry, even kindling a debate regarding whether or not AI doctors may eventually replace human physicians. They argue that the role of doctors will not necessarily be taken over by artificial intelligence in the foreseeable future. However, it will assist them in making better clinical decisions, and in some areas of the industry, such as radiology, it may even fully replace human judgment (Jiang et al., 2017). According to the information that was provided in the article, the implementation of AI in the healthcare industry has been made possible by the growing availability of healthcare data as well as the quick development of methodologies for big data analysis (Jiang et al., 2017). When prompted by the appropriate clinical questions, robust artificial intelligence systems have the ability to discover therapeutically valuable information that is concealed in vast volumes of data. For this reason, the clinical decision-making process can benefit from having access to this information.
Summary of the Article (Objective Review)
The researchers who contributed to this article examined the transformative effects that the development of artificial intelligence has had on the field of healthcare and how it has evolved over the past few decades. In particular, they cited the escalating availability of medical data and the quickening pace of methodological advancement as two examples (Jiang et al., 2017). Despite this, researchers in 2017 did not have a clear understanding of the existing and future applications of AI in the healthcare industry. As a direct consequence of this fact, the primary objective of the authors of the article was to investigate the current state of artificial intelligence applications in the healthcare industry as well as their potential for the future. An additional, more in-depth inquiry into the applications of AI in stroke was carried out by the researchers. The researchers were focused on three primary areas: early detection and diagnosis, therapy, and outcome prediction and prognosis evaluation.
Strengths and Weaknesses of the Article
Strengths
The most valuable aspect of this article is that it analyzed and discussed the most common AI tools for machine learning and natural language processing as potential solutions. This is the article’s greatest strength and the most important addition that the paper makes to the field. The techniques for machine learning can be further broken into two categories: the more standard approaches and the more cutting-edge deep learning techniques. As another key focus of the study, artificial intelligence applications in neurology could be presented and discussed in the article as well (Jiang et al., 2017). These applications were investigated from the perspectives of early disease diagnosis and prediction, therapy, prognosis evaluation and outcome prediction all of which contribute to this field of research.
Weaknesses
The fact that there was an inadequate amount of data interchange is the primary weakness of the study. In order for artificial intelligence (AI) systems to work properly, they need to undergo consistent training using data obtained from clinical trials. The availability of new data is essential for the continued development and improvement of an AI system after it has been initially trained on historical information and has been put into operation. This is because AI systems learn best from examples that are similar to real-world situations. As a result of the fact that the existing healthcare system does not provide individuals with any kind of incentive to share their data on the system, the research was quite difficult to do.
Conclusion and Recommendations
In conclusion, the paper has reviewed the article by focusing on the primary areas of interest. According to the authors of this article, AI can be used effectively to improve healthcare by analyzing a wide variety of healthcare data sets. This article also provides an overview of the various illness categories AI has been used to cure. After that, the authors went into depth on the two primary classifications of AI tools: machine learning (ML) and natural language processing (NLP). In machine learning, the researchers concentrated on the two methods that have shown to be the most successful historically: neural networks and support vector machines (SVM). After that, they analyzed the three primary classifications of AI applications in stroke care.
It is essential, in my opinion, to conduct some research on artificial intelligence in specific sections of a department in order to have a deeper understanding of AI’s significant role in the operations of that particular department. It is more beneficial for researchers to research the topic rather than try to think of any new ideas. The currently available software and hardware linked with AI to advance the department will make it easier for humans to manage their lives.
Reference
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke and vascular neurology, 2(4).