Artificial intelligence or AI represents the capabilities of algorithms for learning from data that enables them to perform automated tasks. According to the World Health Organization, artificial intelligence has promising potential for improving the value advantages of public healthcare and medical research. At the same time, the WHO also points to the necessity of safeguarding ethics of AI in healthcare for reaping the benefits of artificial intelligence.
The capabilities of artificial intelligence to support the interests of patients would depend on success of collective efforts for designing and implementing ethically responsible policies and laws. Most of the ethical issues in the use of AI in healthcare focus on discrimination, privacy and surveillance, human judgment, and bias. Let us explore the different applications of AI in healthcare and how ethics can influence the use of AI in healthcare.
What is the Reason for Adopting AI in Healthcare?
You must understand the role of AI in healthcare before you look for the prospects for ethics in healthcare AI. For example, you must know that nurses are one of the crucial components of the healthcare industry. However, nurses are overworked and underpaid. As the burden of rising chronic diseases increases, healthcare professionals have to encounter resource constraints.
Artificial intelligence could help alleviate the burden on healthcare professionals through automation of administrative and redundant tasks. As a result, healthcare professionals could focus on the problems of patients and other core tasks. Furthermore, AI could also help in changing healthcare systems by producing crucial insights from the massive volumes of digital data.
Want to understand the importance of ethics in AI, ethical frameworks, principles, and challenges? Enroll now in Ethics of Artificial Intelligence (AI) Course
How Can You Implement AI in Healthcare?
The applications of artificial intelligence in healthcare could help you understand its significance. It serves different use cases, including clinical care, health research and drug development, public health surveillance, and health systems planning and management. The importance of answers to “What is ethics in artificial intelligence?” would be evident in how AI changes the traditional processes, systems, and outcomes of healthcare. Here is an overview of the different ways to implement artificial intelligence in healthcare.
-
Clinical Care
The use of artificial intelligence in healthcare focuses on improving diagnostics alongside improving clinical care. For example, AI could utilize patient records to identify vulnerable groups of patients. The use of ethics in AI becomes more important for healthcare as artificial intelligence could help in determining the ideal treatment decisions. In addition, the shift towards home-based care has also emerged as a prominent trend in healthcare that encourages the adoption of AI in healthcare.
-
Drug Development
Artificial intelligence could simplify the process of pharmaceutical development. In addition, AI has the capability to accelerate the pace of drug development. Artificial intelligence could transform drug discovery into a data and capital-centric process rather than depending on labor.
The review of AI ethics examples in drug discovery draws references to the use of robotics alongside data models for genetic targets, diseases, and organs. Artificial intelligence could ensure a cost-efficient drug discovery and development process, thereby emphasizing the necessity of AI ethics.
-
Health Research
Data is a powerful tool for improving the healthcare sector, especially in the use cases of health research. Electronic Health Records or EHR can empower AI-based research with data about patients, their health conditions, and medical history. The applications of AI in EHR could support scientific study, clinical care optimization, and quality improvement.
Prior to the use of clinical support tools, scientific publications, and development of guidelines, it is important to create and train AI with EHR data. The use cases of AI in healthcare could help in uncovering the clinical best practices from electronic health records. As a result, AI could enable new models of clinical practice for delivering healthcare services.
Excited to learn the fundamentals of AI applications in business? Enroll now in AI For Business Course
What are the Ethical Principles for AI in Healthcare?
The growing popularity of the use cases of artificial intelligence in healthcare has created concerns regarding ethics. It is important to identify the values and principles for ethical AI in healthcare. Ethics could also help in addressing the legal issues of AI in healthcare, as artificial intelligence would directly affect human lives.
In addition, the applications of AI in healthcare also have direct consequences on other aspects of the life of patients. For example, concerns regarding the security and confidentiality of the health data of patients are also important. Here is an outline of the important principles that could help in safeguarding ethics in the field of healthcare.
-
Autonomy
Artificial intelligence systems could undermine human autonomy and work independently for some healthcare functions. The use cases of AI in healthcare could transfer the responsibility of decision-making to machines. Therefore, the implementation of ethics of AI in healthcare must focus on autonomy by allowing human users to take complete control of AI systems. It is important to ensure that artificial intelligence does not interfere with the autonomy of patients in any case.
The Human in the Loop or HITL model could help in providing oversight regarding the performance and functionalities of the AI healthcare systems. What would happen when clinical decisions by AI systems and physicians are in conflict with each other? Therefore, ethical AI for healthcare must follow the consent process for all research projects and assessment initiatives.
Patients should be informed about the benefits as well as risks of AI technologies. Transparent and effective monitoring of human values alongside moral consideration at different stages of AI development could help in validating ethics for implementation of AI in healthcare.
-
Trustworthiness
The next important aspect in safeguarding ethics for artificial intelligence in healthcare is trustworthiness. Healthcare professionals could resolve legal issues of AI in healthcare by using trustworthy AI systems. The trustworthiness of an AI solution depends on ensuring that it follows all the relevant laws and regulations. In addition, AI systems in healthcare must be reliable, explainable, and transparent. Artificial intelligence technologies should maintain compliance with legal norms, especially with respect to data and privacy laws.
Take your first step towards learning about artificial intelligence with all the definitions of important AI concepts and terms with simple AI Flashcards.
-
Accountability and Liability
Accountability for ethical AI in healthcare implies that AI systems must be prepared to undergo scrutiny by the concerned authorities. Regular internal and external audits could help ensure optimal functioning of AI technologies. On top of it, the role of AI in healthcare could incorporate the element of accountability by ensuring public availability of audit reports.
Innovators in AI could have doubts regarding medical ethics, regulatory guidelines, and research regulations. Therefore, you must have representatives from the healthcare sector at all stages of developing and deploying AI systems and technologies. It is also important to note that AI systems in healthcare could cause harm in some cases. Therefore, it is important to have an appropriate mechanism for identifying the roles and legal liabilities of all stakeholders in AI healthcare systems.
-
Collaboration
Another important principle for driving ethics in AI for healthcare is collaboration. The use of artificial intelligence for healthcare requires a massive collection of datasets to obtain meaningful outcomes. Therefore, it is important to ensure collaboration between AI experts at the time of research and development. Collaboration between AI researchers and healthcare professionals could boost the process of developing AI systems for healthcare. It also ensures better results for ensuring safety of healthcare data alongside enforcing accountability.
Become a master of generative AI applications by developing expert-level skills in prompt engineering with Prompt Engineer Career Path
-
Non-discrimination and Fairness
Non-discrimination and fairness are also important principles for ensuring implementation of ethical AI in the domain of healthcare. It is important to avoid inaccuracies and biases in AI algorithms to ensure quality. The review of AI ethics examples emphasizes the fact that datasets used for training AI algorithms should have higher accuracy and represent the target population.
AI systems should rely on continuous end-user feedback analysis to minimize inaccuracies and biases. Developers must also ensure that AI should never serve as a tool for exclusion, and AI developers should ensure active inclusion of minority groups and women. In addition, developers must also pay special attention to promotion and safeguards for individual equality. Most important of all, developers should design AI systems and technologies for universal usage.
-
Validity
The principles of AI ethics in the domain of healthcare must also focus on validity. AI technology and systems in healthcare should go through comprehensive clinical and field validation prior to practical applications. You can find answers to ‘What is ethics in artificial intelligence” in the field of healthcare by reflecting on the importance of validity of AI systems.
The discrepancies in working of AI-based algorithms could increase on the basis of differences in datasets for training AI algorithms. It is important to have an internal mechanism for monitoring such discrepancies and conveying the important findings to developers.
The use of AI-based decisions for healthcare could result in potential health hazards or mismanagement issues. Therefore, it is important to have an efficient feedback mechanism for introducing necessary updates, thereby making the AI system a valid choice for healthcare.
Excited to learn about ChatGPT and other AI use cases? Enroll now in ChatGPT Fundamentals Course
-
Accessibility and Inclusiveness
The applications of computers for developing and deploying AI technologies in the domain of healthcare could lead to assumptions regarding broader infrastructure availability. Such type of digital divide could lead to problems with accessibility, especially with the growing dependence on technology.
In such cases, the role of AI in healthcare would point to the need for introducing fairness in distribution of AI technology. Artificial intelligence could lead to discrimination that could create conflicts with fundamental rights of humans. In addition, social, economic, and cultural backgrounds could also define the perspective of patients on AI technologies.
Artificial intelligence technologies should be designed in accordance with variations in gender, income classes, race, and ethnicity. One of the recommended strategies for ensuring inclusiveness points to inclusion of people from different strata of society in development of AI systems.
-
Optimizing Data Quality
Artificial intelligence relies on data, and the effectiveness of AI for healthcare use cases depends on effectiveness of training and testing AI. In the domain of healthcare, training data has formidable significance as discrepancies in training data could create issues with AI functionalities.
For example, the ethics of AI in healthcare point to the issues of data bias, discrimination, and errors. Therefore, it is important to consider the possibilities of biases before deploying AI technologies. It is important to ensure that training data does not have any sampling bias.
Furthermore, the presence of bias in the existing dataset could affect the functionality of AI systems for healthcare. Such types of AI systems must be immediately discontinued, and developers should pay attention to rigorous clinical validation before using AI systems in healthcare.
-
Safety and Risk Reduction
Risks involved with AI technologies in healthcare and clinical research could vary according to the deployment methodology and use cases. Therefore, the implementation of ethics in AI for healthcare should promote safety and risk reduction. You should implement a strong and comprehensive set of control mechanisms to avoid unintended misuse of the AI system.
In addition, you must also have secure systems and software for safeguarding AI applications and sensitive data. Artificial intelligence technologies are also vulnerable targets for cyber-attacks, and their exploitation could lead to unauthorized access to sensitive and private information.
Researchers should also pay attention to rigorous testing of AI technologies and algorithms before implementing them in healthcare. You can also rely on development of an Ethical Committee with the involvement of all stakeholders. It can foster safety of AI systems by providing a reliable benefit-risk assessment.
Identify new ways to leverage the full potential of generative AI in business use cases and become an expert in generative AI technologies with Generative AI Skill Path
Conclusion
The necessity of ethics in artificial intelligence has been advocated by leading organizations. The United Nations and the World Health Organization have created principles for ethical AI. With the implementation of artificial intelligence in the domain of healthcare, you can notice multiple opportunities as well as challenges. One of the biggest highlights in the ethics of AI in healthcare points to compliance with ethical principles. The ethical principles for implementing AI in healthcare include safety, inclusiveness, autonomy, transparency, and accountability.
Ultimately, ethics ensure that AI systems don’t cause intentional or unintentional harm to patients and other stakeholders in the healthcare sector. Artificial intelligence could change healthcare with promising improvements in quality of care. Learn more about the different examples of implementing AI ethics in the domain of healthcare with the help of a credible ethics of AI course right now.
