Abstract

The revolutionary wave of AI has brought this innovation to many industries; it has changed healthcare quite significantly with the evolution in machine learning. The concept of learning from data and taking decisions individually through AI systems increases diagnostic accuracy along with personalizing the drug plan. For example, Google's DeepMind and Project Hanover by Microsoft showed how AI detects cancer tissues and develops customized drugs combinations for diseases such as Acute Myeloid Leukemia. While AI has demonstrated potential to exceed human capabilities, the dependence of AI on reinforcement learning brings new challenges. Unlike traditional AI models, reinforcement learning enables AI to learn from past experiences and adjust autonomously. This feature brings liability issues in medical malpractice cases. If AI systems provide superior diagnostic accuracy, non-utilization by healthcare providers may lead to claims of negligence. At the same time, dependence on AI carries risks unpredictable errors or transparency in the decision-making processes that might undermine patient care and push traditional legal doctrine to their limits. In future, malpractice law may end up dictating AI in standard care and change everything in health care practice. All the same, over-reliance in obscure AI, which was never intended as such, should be discouraged through powerful legal and technical solutions.

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