Harvard AI in Clinical Medicine 2026

• Include:  videos + file sub vtt +  pdf, size:  GB
• Target Audience: pathologists, endocrinologists, ophthalmologists, nurses, gastroenterologists, intensivists, radiologists, surgeons, anesthesiologists, oncologists, pulmonologists, cardiologists, and psychiatrists
• Sample video: contact me for sample video
• Information:

1. Overview

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.

2. Learning Objectives Upon completion of this course, participants will be able to:

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

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

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

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