Ethical Frameworks for AI‐Driven Healthcare

The application of artificial intelligence (AI) in the medical field is arguably the most remarkable achievement of AI technology in the past few decades. It has brought forth new methods to enhance clinical workflows, optimize patient outcomes, and even improve decision-making. Nonetheless, AI technology poses major sociological dilemmas in the field of health professions, particularly in genetics and disease. Applying AI in epidemiology has the potential to change the entire paradigm of how diseases are monitored and detected and how actions to safeguard the public's health are executed. AI-powered algorithms can process immense datasets and detect patterns and trends regarding the spreading of diseases. These patterns and trends can then be used for the formulation of public health policies and programs. However, the use of AI in epidemiology still raises more questions relating to ethical issues very much centered around the privacy and deidentification of data, informed consent, and the possible existence of algorithmic bias. Those insights, however, make stronger the case for accountability, ethical, and equity concept toward the more humane design of AI in epidemiology and public health and for algorithms that rely on such principles. With these principles, AI-informed insights can be used without the risk of misuse while ensuring equitable distribution of resources among communities. Ethical policies concerning AI in areas such as genetics and epidemiology should be guided by transparency, accountability, and equity. These principles will guarantee ethical attention in caregiving that automates processes, increases efficiency, and optimizes patient outcomes.