LEGAL IMPLICATIONS OF AI IN HEALTHCARE: LIABILITY, ACCOUNTABILITY, AND ETHICS

Abstract

The advent of artificial intelligence (AI) in healthcare has revolutionized the clinical decision making process by providing an opportunity to make data-driven therapeutic suggestions. This paper analyzes the trend in therapeutic recommendations based on structured healthcare data and discusses its consequences on the legal framework, such as liability, accountability, and ethical aspects. The relationships between clinical attributes, user generated reviews and demographic characteristics were analyzed through a descriptive analytical method involving use of a structured set of medical records between symptoms, conditions, and recommendations of drugs. The findings indicate that treatment prescriptions depend greatly on the various kinds of medical conditions and that there are a number of medications associated with similar clinical presentations, which is non-deterministic decision-making actions. The number of users rating and the number of reviewers are mostly distributed at higher scales implying the impact of evaluative traits on the pattern of recommendations. Demographic analysis indicates that there is variation between gender and age groups, and the constant availability of side-effect information emphasizes how tricky the choice of treatment is and the impact it has on patient safety. These observed trends bring on the problem of establishing legal responsibility since the variability and weak transparency make it difficult to attribute the liability and accountability in AI-assisted systems. The research highlights the importance of effective governance frameworks that can be used to deliver responsible, transparent, and ethically sound implementation of AI technologies in healthcare systems.