Intelligent Medicine, Uncertain Law: What Every Clinician Needs to Know About AI Regulation

Needs Statement

Artificial intelligence (AI) is a relatively new technology with significant potential in healthcare if implemented in a strategic and safe manner. However, there are challenges in integrating AI into workflows because of limited foundational knowledge, ethical concerns, and insufficient exposure to practical tools that enhance efficiency and innovation. This series aims to bridge the gap between theoretical knowledge and real-world clinical practice, promote the use of research tools such as literature review automation and data analytics, and advance the application of AI-driven educational methods synergistically with the Learning Health System. By focusing on practical applications, ethical challenges, and future innovations, the series seeks to break down barriers to the implementation of AI and to foster innovation and collaboration.

Target Audience

Physicians and other healthcare providers.

Learning Objectives

At the conclusion of the activity, the participants should be able to:
  • Distinguish the current regulatory status of AI-enabled clinical tools under U.S. Food and Drug Administration (FDA) guidance — including Software as a Medical Device (SaMD) and Clinical Decision Support (CDS) software— and identify which categories trigger mandatory oversight versus enforcement discretion.
  • Apply emerging legal standards around medical malpractice and the standard of care to their own clinical practice, including documentation strategies that protect clinicians when artificial intelligence (AI) tools contribute to a diagnostic or treatment decision.
  • Evaluate the State of Texas regulatory framework for AI — including the Texas Responsible Artificial Intelligence Governance Act (RAIGA) and its innovation sandbox provisions — in relation to active state laws in Colorado, California, and New York, and situate these within the federal preemption debate and the European Union (EU) AI Act enforcement timeline.
  • Identify at least three concrete institutional or individual actions clinicians and clinical researchers can take to reduce liability exposure, promote algorithmic fairness, and maintain meaningful human oversight when deploying or using AI tools in healthcare settings.
Course summary
Available credit: 
  • 1.00 AMA PRA Category 1 Credit™
  • 1.00 Ethics
  • 1.00 Participation
Course opens: 
05/01/2026
Course expires: 
05/31/2028
Cost:
$1.00
Rating: 
0

Baylor College of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Baylor College of Medicine designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
This activity has been designated by Baylor College of Medicine for 1 credit of education in medical ethics and/or professional responsibility.

Available Credit

  • 1.00 AMA PRA Category 1 Credit™
  • 1.00 Ethics
  • 1.00 Participation

Price

Cost:
$1.00
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Thank you for reviewing this Baylor College of Medicine on-demand activity. In order to complete the evaluation and claim credit for your participation, please select the appropriate payment option above. After payment has been completed, you will receive an email confirmation and receipt. 

For any questions, contact [email protected].