Harvard AI in Clinical Medicine 2024

Overview

Harvard AI in Clinical Medicine 2024 is a comprehensive on-demand digital education product designed to provide clinicians with foundational knowledge and practical insights into the application of artificial intelligence (AI) in clinical practice. This course covers cutting-edge developments including AI-driven diagnostics, personalized treatment planning, and clinical decision support. On-demand internal medicine education — accessible anytime, anywhere.

The course was originally delivered as a live virtual event featuring expert lectures, panel discussions, and skills stations. Users gain lifetime access to the complete recorded sessions, enabling flexible learning.

Agenda

This agenda reflects the original two-day live virtual course, all times listed in Eastern Time.

• Welcome — Maha Farhat, MD, MSc; Samir Kendale, MD; Isaac Kohane, MD, PhD
• Keynote: Paging Dr. AI: How AI is Changing the Face of Clinical Care — Isaac Kohane, MD, PhD
• Learning the AI Lingo: Machine Learning, Deep Learning, and Large Language Models — Samir Kendale, MD
• The AI Health Care Odyssey: A Historical Expedition — Arjun Manrai, PhD, HMS
• Panel Q&A
• A Look Into the Black Box: Technical Background for Clinicians — Maha Farhat, MD, MSc
• Nuts and Bolts: Medical Data as the Backbone of AI — Monica Agrawal, PhD
• But I Want it Now!: Barriers to Clinical AI Implementation — Genevieve Melton-Meaux, MD, PhD
• Panel Q&A
• AI Learning Revolution: Transforming Medical Education — Adam Rodman, MD
• AI Scribe, Medical Chatbot, and More! — Zachary Lipton, PhD
• How Can I Help You?: Clinical Decision Support — Natalie Pageler, MD, Stanford
• AI as a Crystal Ball: Revolutionizing Risk Stratification — Ziad Obermeyer, MD, Berkeley
• Panel Q&A
• Skills Station – AI Assisted Charting and Patient Communication — Katherine Choi, MD, Abridge
• Skills Station – AI for Heart Failure Management — David Ouyang, MD
• Skills Station – Use Chat GPT to Inform Medical Diagnosis — Adam Rodman, MD
• Welcome — Maha Farhat, MD, MSc; Samir Kendale, MD; Isaac Kohane, MD, PhD
• Keynote: Ethics and AI in Health Care — Rebecca Weintraub Brendel, MD, JD, HMS-MGB
• Law and Regulation in AI — Glenn Cohen, JD
• AI for Pioneering Leadership in the Digital Era — Stanley Shaw, MD, PhD
• Panel Q&A
• Money Talks: How AI Can Help You Improve Revenue — Jean-Claude Saghbini
• AI and Reducing Physician Burnout — Jane Moran
• Panel Q&A
• How AI and ML are Driving Innovation in Biomedicine — Collin Stultz, MD, PhD
• A Picture is Worth A Million Words: AI and Image Processing — Constance Leman, MD, PhD
• Unlocking Drug Discovery: AI’s Pharmaceutical Frontier — Vikram Khurana, MD, PhD
• Precision Medicine: AI and Personalized Treatment in Oncology — Eli Van Allen, MD
• Panel Q&A
• Clinical Applications: Pathology — Kun Yu, MD, PhD
• Clinical Applications: Dermatology — Roxana Daneshjou, MD, PhD
• Clinical Applications: Gastroenterology — Tyler Berzin, MD
• Clinical Applications: Radiology — Pranav Rajpurkar, PhD
• Clinical Applications: Ophthalmology — Nazlee Zebardast, MD, MS
• Clinical Applications: Critical Care — Leo Celi, MD
• Final Remarks — Maha Farhat, MD, MSc; Samir Kendale, MD; Isaac Kohane, MD, PhD

Learning Objectives

• 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 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.

Speakers

• Maha Farhat, MD, MSc
• Samir Kendale, MD
• Isaac Kohane, MD, PhD
• Arjun Manrai, PhD — Harvard Medical School
• Monica Agrawal, PhD
• Genevieve Melton-Meaux, MD, PhD
• Adam Rodman, MD
• Zachary Lipton, PhD
• Natalie Pageler, MD — Stanford University
• Ziad Obermeyer, MD — University of California, Berkeley
• Katherine Choi, MD — Abridge
• David Ouyang, MD
• Rebecca Weintraub Brendel, MD, JD — Harvard Medical School and Mass General Brigham
• Glenn Cohen, JD
• Stanley Shaw, MD, PhD
• Jean-Claude Saghbini
• Jane Moran
• Collin Stultz, MD, PhD
• Constance Leman, MD, PhD
• Vikram Khurana, MD, PhD
• Eli Van Allen, MD
• Kun Yu, MD, PhD
• Roxana Daneshjou, MD, PhD
• Tyler Berzin, MD
• Pranav Rajpurkar, PhD
• Nazlee Zebardast, MD, MS
• Leo Celi, MD

Target Audience

This course is designed for a broad range of health care professionals including physicians, nurses, nurse practitioners, and clinical leaders such as administrators who are involved in patient care across diverse clinical settings. It is intended for those seeking to build expertise in applying artificial intelligence in clinical medicine to enhance diagnostic accuracy, patient outcomes, and treatment customization.

Why This Topic Matters

Harvard AI in Clinical Medicine 2024 addresses the growing integration of artificial intelligence within the specialty of internal medicine and related clinical fields. As AI technologies increasingly influence patient care and medical research, understanding their applications, benefits, and limitations is essential for contemporary clinical practice.

MedHub Central provides access to this program enabling clinicians to deepen their understanding of AI’s role in modern medicine, reinforcing professional competence. This course supports informed clinical decision-making in the evolving landscape of AI-enhanced health care.