Answer To: Literature Review Topic: Ethical Issues of Artificial Intelligence Medical ApplicationWritten...
Ayan answered on Apr 12 2023
WRITTEN ASSIGNMENT 29
WRITTEN ASSIGNMENT
Table of contents
Introduction 4
Literature Review 5
Overview of AI in Healthcare 5
Historical development of AI in healthcare 6
Types of AI in healthcare 7
Advantages and limitations of AI in healthcare 9
Advantages of AI in healthcare 9
Limitations of AI in healthcare 10
Regulations and policies governing the use of AI in healthcare 10
Ethics of AI-based medical diagnosis and treatment 12
AI in clinical decision-making: benefits and risks 13
Benefits 13
Risks 14
AI and medical error reduction 15
Patient attitudes and perceptions towards the use of AI in healthcare 16
Professional attitudes and perceptions towards the use of AI in healthcare 18
AI in medical education and training 19
Privacy and Confidentiality 21
Bias and Discrimination 23
Accountability and Responsibility 24
Informed Consent 26
Autonomy and Human Agency 27
Technical Considerations 29
Case Studies 30
Stakeholder Perspectives 31
Ethical Frameworks 32
Global Perspectives 34
Future Directions 35
Conclusion 36
References 38
Introduction
The potential for artificial intelligence (AI) to completely transform the healthcare sector has been recognized. AI-based medical applications can enhance the precision, effectiveness, and timeliness of diagnosis, treatment, and care delivery, resulting in better patient health outcomes. For instance, AI-powered chat bots may provide consumers individualized health advice, and AI-based medical imaging technologies can help medical practitioners diagnose illnesses more effectively and accurately. Despite the potential advantages, applying AI to healthcare presents a number of moral concerns that must be resolved in order to assure its ethical and safe application. Privacy and confidentiality are two of the main ethical issues with AI in healthcare. Sensitive personal health data is collected and processed by AI-based medical apps, which raises questions regarding data privacy and confidentiality. Healthcare organizations are required to preserve patient information, make sure it is not disclosed to unauthorized parties, and notify patients about how their information is used.
Bias and prejudice are a serious ethical problem. Healthcare prejudice and bias can be reinforced and even amplified by AI systems. For instance, algorithms that have been trained on biased datasets may produce incorrect diagnoses or treatment suggestions. Healthcare organizations must make sure that AI algorithms are created and trained on a variety of fair datasets to prevent bias. Accountability and responsibility are further issues that are brought up by the usage of AI in healthcare. It is unclear who should be held accountable in the event that an AI-related adverse occurrence occurs the developer, the healthcare system, or the AI system itself. The usage of AI in healthcare organizations must be governed by explicit policies, and responsibility must be delegated to guarantee accountability.
Additionally, concerns regarding informed consent, autonomy, and human agency are raised by the use of AI in healthcare. Patients must be made aware of the use of AI in their treatment and given the choice to reject AI-based judgments. Healthcare practitioners must make sure that using AI does not impair either patient autonomy or their professional judgment. This report of the literature tries to give a thorough overview of the moral dilemmas surrounding AI in medical applications. Various ethical issues, such as privacy and secrecy, bias and discrimination, accountability and responsibility, informed consent, and autonomy and human agency will all be covered. It will also go through current ethical frameworks and standards for the use of AI in healthcare and talk about new trends and potential future paths. Healthcare organizations can assure the safe and moral application of AI in healthcare and advance patients' wellbeing by recognizing and resolving these ethical concerns.
Literature Review
Overview of AI in Healthcare
The use of artificial intelligence (AI) in healthcare has grown in importance since it offers a number of potential advantages, including better diagnosis and treatment, enhanced effectiveness, and lower costs. Some significant applications of AI in healthcare include –
· X-rays, CT scans, and MRIs are just a few examples of the medical imagery that AI is being used to analyze in order to spot problems and help with diagnosis. This may shorten the time it takes to make a diagnosis and increase the accuracy of such diagnoses.
· Clinical decision-making: AI is being utilized to enhance healthcare professionals' decision-making, enabling them to make more accurate and fact-based choices on patient care. Large volumes of patient data and medical literature may be analyzed by AI algorithms to produce suggestions for care in terms of diagnosis, therapy, and other areas.
· Electronic health records: Electronic health records (EHRs), which may include a lot of patient data, are managed with the use of AI (Beil et al., 2019). AI algorithms may aid in the collection, organization, and analysis of this data, revealing information about the health of the patient and pointing up possible areas for treatment improvement.
· Drug discovery: AI is being utilized to aid in the process, assisting in the identification of possible drug targets and the prediction of the efficacy of novel medications. This can hasten the process of developing new drugs and increase the likelihood that they will be successful.
· Personalized medicine: Artificial intelligence (AI) is being used to examine patient data and find personalized treatment solutions based on unique traits like genetics and medical history. This can increase treatment efficiency and lower the possibility of negative side effects.
· Robotics: Artificial intelligence (AI) is being utilized to create robotic devices that can help with a variety of healthcare activities, including surgery and rehabilitation. Compared to manual approaches, robotic systems can provide more accuracy, control, and consistency.
Historical development of AI in healthcare
Researchers first started investigating the use of computers in medical diagnosis and treatment in the 1960s, which is when artificial intelligence (AI) in healthcare first started to take shape. Rule-based systems, which relied on collections of if-then statements to detect and treat medical disorders, were among the early uses of AI in healthcare. These systems' limitations, however, stemmed from their incapacity to manage intricate medical issues and the necessity of labour-intensive manual programming. Significant advancements in the application of AI in healthcare were made in the 1980s and 1990s thanks to the development of machine learning algorithms. Large datasets may be used to find patterns and correlations that machine learning algorithms can then learn from and improve over time. This technology made it possible to create decision support systems that could offer real-time suggestions to physicians based on patient data. As computer vision and natural language processing (NLP) technology advanced in the 2000s, the application of machine learning algorithms in healthcare grew (Guan, 2019). Computer vision enables computers to recognize and analyze pictures and videos, whereas NLP helps computers to comprehend and interpret human language. These technologies have made it possible to create sophisticated medical imaging systems, which can aid physicians in precisely and speedily detecting and diagnosing disorders. The advancement of deep learning algorithms in recent years has increased the application of AI in healthcare. Deep learning algorithms can digest a lot of data and learn from experience since they are made to resemble the human brain. With the use of this technology, AI systems have been created that are capable of predicting patient outcomes, locating medication targets, and enhancing clinical decision-making. Generally speaking, improvements in processing power, data analytics, and machine learning algorithms have propelled the historical growth of AI in the healthcare industry. The potential uses of AI in healthcare are anticipated to grow as these technologies advance, revolutionizing the way healthcare is provided and enhancing patient outcomes.
Types of AI in healthcare
There are several types of AI used in healthcare. Here is a brief description of some of the most common types:
· Machine learning is a sort of artificial intelligence that enables computers to gain knowledge from experience and enhance their performance over time. Machine learning algorithms can analyze enormous volumes of data in the healthcare industry to find patterns and associations that can aid physicians in the diagnosis of illnesses, the prediction of patient outcomes, and the personalization of treatment regimens.
· NLP, a subset of artificial intelligence, enables computers to comprehend and interpret human language (Fiske, Henningsen & Buyx, 2019). NLP may be used in the healthcare industry to analyse patient records and other unstructured data sources in order to extract pertinent data that would assist physicians in making better judgments.
· Robotics: Robotics is a subset of AI that makes use of autonomous machines to carry out tasks. Robotics can be utilized in the healthcare industry to help with procedures, patient rehabilitation, and other jobs that call for accuracy and precision.
· Computer vision is a form of AI that enables machines to identify and examine pictures and movies. In the field of healthcare, computer vision may be used to examine pictures from diagnostic tests like X-rays, MRIs, and CT scans in order to find anomalies and identify illnesses.
· Expert systems are a subset of AI that provides suggestions for diagnosis and therapy using rule-based reasoning. Expert systems in healthcare may be utilized to give doctors real-time decision support based on patient data.
· Catboats are an AI technology that uses natural language processing to converse with patients and present them with health-related information. Chatbots can be used in the healthcare industry to schedule appointments, remind patients to take their medications, and offer answers to frequently asked patient inquiries.
· Virtual assistants are AI-powered solutions that can aid healthcare workers in more effectively managing their workload. Virtual assistants are useful in the healthcare industry for managing patient information, scheduling appointments, and other administrative duties.
· Wearable’s and sensors: These AI-powered gadgets may gather information about a patient's health state in real-time. Wearable’s and sensors in healthcare can be used to manage medication compliance, monitor vital signs, and spot early illness symptoms.
Advantages and limitations of AI in healthcare
Advantages of AI in healthcare
· Greater precision and accuracy: AI-powered systems can analyze enormous quantities of medical data and spot trends that human physicians would overlook. Better patient outcomes, more precise diagnoses, and more individualized treatment strategies can result from this.
· Decision-making that is quicker and more effective is possible because to AI algorithms, which can digest data considerably more quickly than humans can (Safdar, Banja & Meltzer, 2020). This enables healthcare professionals to take quicker and better judgments. In emergency situations, where split-second judgments might mean the difference between life and death, this can be very crucial.
· AI can assist improve patient outcomes and lower the risk of problems by providing more precise diagnosis and personalized treatment strategies.
· Saving money: AI-powered solutions can save healthcare costs by increasing productivity and lowering the need for expensive diagnostic procedures and treatments.
· Greater access to care: AI can increase access to care by facilitating telemedicine, remote consultations, and other cutting-edge healthcare delivery methods.
Limitations of AI in healthcare
· Lack of regulation: Due to the quick speed of AI invention, regulatory frameworks have not kept up with it, leaving many AI-powered technologies untested and uncontrolled. Concerns concerning patient effectiveness and safety may arise as a result.
· Data bias and quality: To learn and make choices, AI systems rely on a lot of data. Results may be erroneous or biassed if this data is of low quality or is biassed in any way.
· Legal and ethical issues: AI-powered products may give rise to a number of legal and ethical issues, such as accountability, informed consent, and privacy (Keskinbora, 2019).
· Limited clinical validation: The efficacy and safety of many AI-powered technologies have not been thoroughly examined in clinical studies.
· Integration difficulties: Adapting processes, data systems, and clinical procedures in order to integrate AI-powered solutions into current healthcare systems can be difficult.
· Patient acceptance: Patients may be reluctant to utilize AI-powered healthcare solutions, especially if they believe they will replace human physicians or invade their privacy.
Regulations and policies governing the use of AI in healthcare
· The US Food and Drug Administration (FDA) started its Digital Health Precertification Program in 2017 to offer a faster process for the clearance of digital health technology, including AI-powered solutions. The program allows businesses to be pre-certified based on their organizational excellence and culture of quality, which can hasten the clearance process for new AI-powered healthcare technologies.
· The EU's General Data Protection Regulation (GDPR), which went into force in 2018, is a comprehensive data protection rule. It lays forth strong guidelines for the storage, use, and sharing of personal data and mandates that organizations seek individuals' express consent before collecting and using such data. All businesses doing business in the EU, including those creating and using AI-based healthcare products, must comply with the GDPR.
· The Global Strategy on Digital Health of the World Health Organization (WHO), which was adopted in 2020, lays forth a framework for the responsible and moral use of digital health technologies, including AI. The plan places emphasis on the necessity of making sure that digital health technologies are fair, evidence-based, and focused on individuals.
· The National Institute for Health and Care Excellence (NICE) in the UK is in charge of creating standards and recommendations for healthcare in that country (Schönberger, 2019). Guidelines for the use of AI-powered technologies in healthcare were released by NICE in 2021, outlining recommended practices for the creation, testing, and usage of these systems.
· The Code of Medical Ethics of the American Medical Association (AMA): The AMA's Code of Medical Ethics offers instructions on the moral obligations of doctors and other healthcare workers. The code offers particular recommendations for applying AI to healthcare, highlighting the significance of openness, responsibility, and patient safety.
· The Institute of Electrical and Electronics Engineers (IEEE) has created a set of standards for the ethical design and use of AI-powered devices. These rules, referred to as Ethically Aligned Design, address a number of issues, including as responsibility, prejudice, and openness.
In general, laws and rules governing the application of AI in healthcare are still being developed. These rules and guidelines must be developed and improved as AI-powered healthcare solutions become more commonplace in order to guarantee that AI is applied in a responsible, ethical, and safe way.
Ethics of AI-based medical diagnosis and treatment
· Transparency and explain ability: The lack of openness and explain ability in how the algorithms arrive at their judgments is one of the main ethical problems with AI-based medical diagnosis and therapy. To make wise judgments and interact with patients successfully, healthcare personnel need to be able to comprehend how the AI generates its suggestions.
· Discrimination and prejudice: There is a chance that AI-based medical diagnosis and treatment might worsen discrimination and bias, especially if the algorithms are developed on biassed data sets. It's crucial to make sure that the data utilized to train these algorithms is varied and accurately represents the target audience.
· Privacy and security issues are major ones since AI-based medical diagnosis and treatment primarily rely on the gathering and analysis of patient data (Char, Abràmoff & Feudtner, 2020). Organizations that provide healthcare services must take precautions to secure patient data against unauthorized access, use, and disclosure.
· Patients must provide their informed permission before AI-based diagnosis and therapy are employed, just like with any other medical procedure. Consequently, patients must be thoroughly informed of the drawbacks, advantages, and hazards of these technologies and must be given the choice to forego using them.
· Responsibility and accountability: For AI-based medical diagnosis and treatment, it is necessary to create distinct lines of accountability. Healthcare practitioners are liable for any mistakes or injury brought about by the AI algorithms they utilize, and they must accept responsibility for the judgments made by these technologies.
· Autonomy and human agency: By minimizing the influence of medical experts in decision-making, AI-based medical diagnosis and treatment run the danger of undermining patients' autonomy and agency. Making ensuring that patients stay at the centre of their own treatment and that AI is used to supplement rather than replace healthcare personnel are both crucial.
AI in clinical decision-making: benefits and risks
Benefits
· Greater accuracy: AI can rapidly and correctly analyze enormous volumes of data, which can assist medical personnel in making more accurate diagnosis and treatment choices.
· Greater effectiveness: AI may make healthcare professionals' job more effective and error-free by automating repetitive activities like data input and analysis.
· Personalized care: By examining a patient's medical background, genetic information, and lifestyle choices, AI can assist medical providers in customizing treatment strategies for specific individuals.
· Predictive analytics: AI can assist medical personnel in identifying individuals who are likely to experience the onset of specific illnesses, enabling early intervention and prevention.
· AI may help healthcare organizations cut costs by increasing efficiency and minimizing the need for pointless tests and procedures.
Risks
· If AI algorithms are trained on biased data sets, they run the danger of becoming prejudiced, which might result in unfair or incorrect diagnoses and treatment recommendations.
· Lack of transparency might make it difficult for healthcare practitioners to guarantee that patients are making informed decisions since AI algorithms can be tricky to comprehend and interpret.
· AI relies on a considerable quantity of patient data, which creates serious privacy and security risks (Davenport & Kalakota, 2019). Patient data must be safeguarded against unauthorized access, use, and disclosure by healthcare organizations.
· Legal and regulatory difficulties, such as those involving responsibility and accountability, are brought up by the application of AI in clinical decision-making.
· Informed consent, autonomy, and human agency are just a few of the ethical challenges that the use of AI in healthcare decision-making brings up.
The use of AI to clinical decision-making raises moral concerns about responsibility, prejudice, and informed consent. While AI may increase the precision and effectiveness of clinical decision-making, there is...