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Related Experiment Video

Updated: Aug 3, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Foundation models for generalist medical artificial intelligence.

Michael Moor¹, Oishi Banerjee², Zahra Shakeri Hossein Abad³

¹Department of Computer Science, Stanford University, Stanford, CA, USA.

|April 12, 2023

Summary

This summary is machine-generated.

We introduce generalist medical artificial intelligence (AI) models capable of performing diverse medical tasks with minimal data. These flexible AI systems interpret various data types to provide advanced medical reasoning and explanations.

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Published on: June 13, 2025

Area of Science:

Medical Artificial Intelligence
Machine Learning in Healthcare

Background:

Rapid advancements in artificial intelligence (AI) are enabling new medical applications.
Current AI models often require extensive task-specific labeled data.

Purpose of the Study:

To propose a new paradigm: generalist medical AI (GMAI).
To outline the capabilities and requirements for GMAI models.

Main Methods:

Developing self-supervised learning on large, diverse datasets.
Enabling flexible interpretation of multimodal medical data (imaging, EHR, genomics, text, etc.).

Main Results:

GMAI models can perform diverse tasks with little to no task-specific data.
GMAI can produce expressive outputs like free-text explanations and image annotations.
Identified high-impact applications and necessary technical capabilities for GMAI.

Conclusions:

GMAI represents a significant shift in medical AI capabilities.
GMAI will necessitate new approaches for AI regulation, validation, and data collection practices.