Chat M.D

Area of Science:

• Artificial Intelligence in Medicine
• Legal and Ethical AI Governance
• Healthcare Quality Assurance

Background:

• Large language models (LLMs) show potential in healthcare but exhibit unreliability and safety concerns.
• Existing AI guidelines lack clear pathways for legal implementation in medical applications.
• Current focus is on AI model standards, not their translation into enforceable law.

Purpose of the Study:

• To propose a legal and regulatory framework for governing LLMs in medical diagnosis and treatment.
• To evaluate the applicability of existing medical quality assurance frameworks to AI governance.
• To establish measurable standards for AI safety, performance, and accountability in healthcare.

Main Methods:

• Analysis of current AI guidelines and their limitations for legal application.
• Evaluation of existing medical quality frameworks for potential adaptation to AI.
• Exploration of legal and economic incentives (reimbursement, liability) for AI compliance.

Main Results:

• Identifies gaps in current AI standards and the need for legal transmutation.
• Suggests adapting medical quality frameworks for AI governance.
• Proposes a dual approach: treating LLMs as professionals and products with measurable standards.

Conclusions:

• LLMs in medicine need oversight comparable to human professionals and products.
• Measurable safety and performance standards, coupled with legal liability and incentives, are crucial.
• This approach offers a path toward genuine oversight and safe integration of LLMs in US healthcare.