Anesthesiologists and anesthesiologists-in-training and other anesthesia professionals, nurse anesthetists and anesthesia assistants.

Upon completion of this activity, participants should be able to:

• Describe the mechanisms by which large language models generate text in response to prompts.
• Evaluate the benefits and limitations of applying large language models to clinical decision-making scenarios.
• Analyze the potential impact of large language models on personal clinical practice, including opportunities and challenges.

Suggested Additional Reading:

1. Goh, E., Gallo, R.J., Strong, E. et al. GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trial. Nat Med 31, 1233–1238 (2025). https://doi.org/10.1038/s41591-024-03456-y
2. Thirunavukarasu, A.J., Ting, D.S.J., Elangovan, K. et al. Large language models in medicine. Nat Med 29, 1930–1940 (2023). https://doi.org/10.1038/s41591-023-02448-8
3. Lee P, Bubeck S & Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N. Engl. J. Med. 388, 1233–1239 (2023).

Joint Accreditation Statement:

In support of improving patient care, the University of Pittsburgh is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

The University of Pittsburgh School of Medicine is accredited by the ACCME to provide continuing medical education for physicians. The University of Pittsburgh School of Medicine designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Each physician should only claim credit commensurate with the extent of their participation in the activity. 

Other health care professionals will receive a certificate of attendance confirming the number of contact hours commensurate with the extent of participation in this activity.