Empowering Faculty to Effectively Engage with AI and Learners in Clinical Settings – Co-sponsored by the Geisel Academy of Educators and Scholars

This session examines the integration of artificial intelligence into clinical practice, emphasizing its benefits, limitations, and ethical considerations. Participants will explore effective prompting techniques, evaluate AI-generated recommendations for validity and bias, apply critical thinking through case-based discussions, and learn strategies for supervising trainees using AI in clinical settings. The program highlights the importance of shared decision-making, maintaining clinical judgment, and recognizing the risks of overreliance on AI. Learners will also review best practices for protecting patient privacy, including HIPAA-compliant use of large language models and safeguarding confidential health information when using AI tools in clinical care.

Presenters

Josie Fisher, MD, MPP, is a primary care physician in the General Internal Medicine 3M Clinic at Dartmouth Hitchcock Medical Center and serves as Associate Program Director for Ambulatory Education in the Internal Medicine Residency. After earning her MD and Master of Public Policy degrees from Harvard and completing her internal medicine residency and chief residency at Massachusetts General Hospital, she joined the faculty in 2024 and focuses on improving access to primary care and coordination between primary and specialty care, including care for patients with substance use disorders.

David M. Haughey, MD, is a hospitalist at Dartmouth Hitchcock Medical Center, where he has been on faculty since 2018 and serves as Associate Program Director and Director of Internal Medicine Resident Point-of-Care Ultrasound (POCUS) Training and Education. He earned his medical degree from SUNY Upstate Medical University after completing a biochemistry degree at Hobart College, and his clinical and research interests focus on POCUS, procedural education, patient safety, and high-value care for hospitalized patients.

Download the slides HERE.

1. Describe best practices for prompting artificial intelligence (AI) large language models (LLMs) when asking clinical questions.
2. Discuss how to help learners recognize potential biases and weaknesses of LLMs, including how this affects the internal and external validity of LLM outputs.
3. Discuss the risk of de-skilling, never-skilling, and mis-skilling for all levels of learners, including faculty.

In support of improving patient care, Dartmouth Health 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. 

American Medical Association (AMA)
Dartmouth Health designates this Enduring Material for a maximum of 1.00 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. 

All other learners may claim CME-designated participation credit. Consult your professional licensing board regarding the applicability and acceptance of CME-designated participation credit for programs certified for credit by organizations accredited by Joint Accreditation for Interprofessional Education.

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Bibliographic Sources

• Stelling BV, Stephenson C, Daum J. Beyond the Black Box: A Framework for AI Literacy and Critical Appraisal in Residency Education. Slides presented at: AIMW Conference; April 21, 2026; Seattle, WA. Accessed May 20, 2026.

• Abdulnour RE, Gin B, Boscardin CK. Educational Strategies for Clinical Supervision of Artificial Intelligence Use. N Engl J Med. 2025;393(8):786-797. https://doi.org/10.1056/NEJMra2503232

• Doshi SK, Kar S, Nair DG, et al. Left Atrial Appendage Closure or Anticoagulation for Atrial Fibrillation. N Engl J Med. 2026;394(21):2083-2094. https://doi.org/10.1056/NEJMoa2517213

• Landmesser U, Skurk C, Kirchhof P, et al. Left Atrial Appendage Closure or Medical Therapy in Atrial Fibrillation. N Engl J Med. 2026;394(13):1270-1280. https://doi.org/10.1056/NEJMoa2513310

• Marcus GM. Left Atrial Appendage Closure - Should Recommendations Be Expanded?. N Engl J Med. 2026;394(21):2156-2157. https://doi.org/10.1056/NEJMe2603846

• Hedman M, Kosuta V, Lindmark M, et al. Diagnostic accuracy of otitis media with and without a fictitious AI support among physicians in primary care and medical students. Scand J Prim Health Care. 2026;44(1):1-13. https://doi.org/10.1080/02813432.2025.2571936

• American Medical Association, American Medical Association. 2 in 3 physicians are using health AI—up 78% from 2023. American Medical Association. https://www.ama assn.org/practice-management/digital-health/2-3-physicians-are-using-health-ai-78-2023?utm_source=chatgpt.com. Published February 26, 2025.

• Sengupta N, Feuerstein JD, Jairath V, et al. Management of patients with acute lower gastrointestinal bleeding: An updated ACG guideline. Am J Gastroenterol. 2023;118(2):208-231

• Tomaselli GF, Mahaffey KW, Cuker A, et al. 2020 ACC expert consensus decision pathway on management of bleeding in patients on oral anticoagulants: A report of the American college of cardiology solution set oversight committee. J Am Coll Cardiol. 2020;76(5):594-622. 10.

• Li G, Abbade LPF, Nwosu I, et al. A scoping review of comparisons between abstracts and full reports in primary biomedical research. BMC Med Res Methodol. 2017;17(1):181. 11.

• SHM Converge 2026. “Prompt Perfect: AI-Powered Charts, Reasoning Rhythms, and Doc Tracks from the Music City Escape Room.” S Tilak, G Liu and M Issa.

Provider Contact Information
Provider contact information for questions regarding accreditation of the activity:

Center for Learning and Professional Development
ACE Office
Dartmouth Health
[email protected]

Mitigation of Relevant Financial Relationships

Dartmouth Health adheres to the ACCME’s Standards for Integrity and Independence in Accredited Continuing Education.  Any individuals in a position to control the content of a CE activity, including planners, faculty, authors, or others are required to disclose all financial relationships with ineligible companies^.  All relevant financial relationships have been mitigated prior to the commencement of the activity.

^Ineligible Company is any entity whose primary business purpose is producing, marketing, selling, re-selling or distributing healthcare products used by or on patients.