Harvard AI in Clinical Medicine 2026

1. Overview
Over the last few decades, the digitization of medical records has created opportunities for automation and data-driven clinical support for a wide range of routine clinical applications. Today, artificial intelligence (AI) is accelerating innovation in health care. We are on the cusp of revolutionizing how we care for patients in profound ways.
New technologies are available now to help you impact your practice. AI medical scribes, new research tools and diagnostic tests, and personalized treatment options are just a few applications of AI that are beginning to have a direct impact on clinicians and the patients they serve.
Up until now, most health care providers have not received formal training in artificial intelligence. Now is the time for physicians, nurses, advanced practice providers, and all allied health professionals to prepare for how AI is changing medical care.
This live online course focuses on cutting edge and exciting new applications of AI, including foundational principles and lessons learned that you will be able to take directly back to your practice. Over three days, you will hear from medical society leaders, academic leaders, and innovators from academia and industry.
Sessions will dive into the applications of AI in diagnosing diseases, predicting patient outcomes, patient monitoring, and personalizing treatment plans. Through lectures, field-specific break-out sessions, and real-world case studies, you will acquire the knowledge to harness AI’s potential to advance patient care, research, and health systems innovation. Faculty experts will explore the ethical implications, challenges, and opportunities inherent in integrating AI into modern health care practice.
During this course we will cut through the hype around AI to provide realistic, firsthand viewpoints into the potential of AI in clinical practice. Health care professionals, including physicians, nurses, nurse practitioners, physician assistants, and other practitioners are highly encouraged to join us for this transformational learning experience.
2. Learning Objectives Upon completion of this course, participants will be able to:

Define the unique challenges and opportunities for integrating AI in specialized health care fields.

Discuss the ethical considerations and potential biases in AI algorithms, especially in decision-making processes related to patient care, diagnosis, and treatment planning.

Review the current status area of AI regulation and how it can impact health care.

Assess the long-term quality and accuracy of AI technologies and their impact on patient care.

Develop methods for integrating AI into medical education, including content generation, evaluation, and ensuring alignment with educational objectives.

3. Target Audience

Who Should Participate: Clinicians treating patients in any type of setting including, physicians, nurses, nurse practitioners, and clinical leaders.
Best for pathologists, endocrinologists, ophthalmologists, nurses, gastroenterologists, intensivists, radiologists, surgeons, anesthesiologists, oncologists, pulmonologists, cardiologists, and psychiatrists.

4. Topics

Keynote: Paging Dr. A.I.: How AI is Changing the Face of Clinical Care

Learning the AI Lingo: Machine Learning, Deep Learning, and Large Language Models

A Look Into the Black Box: Technical Background for Clinicians

Medical Data as the Backbone of AI

Chatbots in Health Care: A Historical Expedition

AI Learning Revolution: Transforming Medical Education

Ambient Scribes

An AI designed drug for IPF: from preclinical development to phase II in under 3 years

Precision Medicine: AI and Personalized Treatment in Oncology

AI Powered Drug Repositioning and Clinical Trial Design

Keynote: Ethics and AI in Healthcare

AI for Pioneering Leadership in the Digital Era

Law and Regulation in AI

Telemetry/Mhealth for early detection of heart failure exacerbation

Brain computer interfaces and decoding speech

Can Chat bots improve mental health?

Bias in Risk Stratification for allocation and policy

Robust, Fair and private AI

Algorithmic bias in clinical scores and implications for AI

But I Want it Now!: Barriers to Clinical AI Implementation

How Can I Help You? Clinical Decision Support in the EHR

Implementing AI in your small practice

Hidden risks of AI in your practice

Money Talks: How AI Can Help You Improve Your Bottom Line

AI and Reducing Healthcare Provider Burnout

Why AI may be good for our health but hurt our wallets

Study Hall – Clinical Applications: Pathology

Study Hall – Clinical Applications: Endocrinology

Study Hall – Clinical Applications: Ophthalmology

Study Hall – Clinical Applications: Nursing

Study Hall – Clinical Applications: Gastroenterology

Study Hall – Clinical Applications: Critical Care

Study Hall – Clinical Applications: Radiology

Study Hall – Clinical Applications: Surgery or Anesthesia

Virtual demonstrations: Ambient Scribe from Abridge

Virtual demonstrations: Leveraging clinician’s expertise with Agentic AI

Virtual demonstrations: Evidence search using OpenEvidence

Virtual demonstrations: Doctronic

Virtual demonstrations: Uptodate Expert AI

Virtual demonstrations: Glasshealth’s clinical decision support platform