July 24, 2026 - Explainability, Trust, and Human-AI Interaction

July 24, 2026

Goal: Promote appropriate trust and adoption

  • Black-box vs interpretable models  
  • Explainability tools (conceptual overview)
  • Clinician trust and cognitive burden
  • Automation bias and alert fatigue
  • Human-in-the-loop vs human-on-the-loop systems

Discussion: When not to use AI

Target Audience

This activity is intended for physicians, nurses, quality & safety professionals, healthcare administrators & pharmacists.

Learning Objectives

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

  1. Develop a shared AI vocabulary and conceptual foundation
  2. Describe the data that powers AI systems and identify their limitations
  3. Relate core AI concepts to real‑world clinical and operational workflows
  4. Describe the value of AI within and beyond direct clinical care
  5. Critically assess AI tools for appropriateness, performance, and risk
  6. Apply principles that ensure safe and equitable use of AI
  7. Integrate appropriate trust in, and adoption of, AI-enabled tools
  8. Identify rapidly emerging AI tools that clinicians are already using
  9. Measure the impact and value of AI tools in healthcare settings
  10. Integrate AI use with standards of clinical professionalism

Additional Information

Provided by: 

Center for AI and Biomedical Informatics in a Learning Health System (CAIBILS) & Mass General Brigham
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Course summary
Course opens: 
05/01/2026
Course expires: 
09/28/2026
Event starts: 
07/24/2026 - 12:00pm EDT
Event ends: 
07/24/2026 - 2:00pm EDT
Cost:
$0.00
Part of: 
AI Fundamentals for Healthcare Professionals
Add to calendar: 

Course Co-Directors

David W. Bates, MD, MSc
Director, Center for Artificial Intelligence and Bioinformatics in the Learning Healthcare System (CAIBILS), Mass General Brigham
Professor of Medicine, Harvard Medical School

Hossein Estiri, PhD
Investigator, Massachusetts General Hospital
Associate Professor of Medicine, Harvard Medical School

Li Zhou, MD, PhD
Director, MTERMS Lab, Mass General Brigham
Lead Investigator, Brigham and Women’s Hospital
Professor of Medicine, Harvard Medical School

Speakers

David W. Bates, MD, MSc
Director, Center for Artificial Intelligence and Bioinformatics in the Learning Healthcare System (CAIBILS), Mass General Brigham
Professor of Medicine, Harvard Medical School

Hossein Estiri, PhD
Investigator, Massachusetts General Hospital
Associate Professor of Medicine, Harvard Medical School

In support of improving patient care, Mass General Brigham 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.

Credit Designation Statements

AMA PRA Category 1 CreditsTM
Mass General Brigham designates this live activity for a maximum of 26.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Nursing
Mass General Brigham designates this activity for 26.00 ANCC contact hours. Nurses should only claim credit commensurate with the extent of their participation in the activity.

Pharmacy
This activity provides 26.00 contact hours (26.00 CEUs) of continuing education credit. ACPE Universal Activity Number (UAN): JA0007437-0000-26-008-L99-P

Price

Cost:
$0.00
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