Abstract: Journal of Clinical Epidemiology and Public Health

Abstract:
For data-intensive analysis and knowledge-based management, artificial intelligence requires knowledge and skills about data quality. The advancement of computer science through the incorporation of innovation in fields like epidemiology, biology, medicine, and public health is necessary for AI to help the larger purpose of public health. Applications fall into three main categories: administrative tasks, patient engagement and adherence, and diagnosis and treatment suggestions. AI implementation is required to improve the effectiveness of managing health services and making medical judgments. AI could be used in the healthcare industry to improve patient care, particularly in handling massive data, exponential computer power, and the always rising demand on the health care system. The main concern is making sure that the solutions being created have the ability to benefit everyone equally, balance risk and reward, and reduce dependence. It is challenging to facilitate initial adoption and continuous deployment in the healthcare system, so we examine some of the moral dilemmas that AI clinical applications face.