Summary

This repository serves as a comprehensive, curated collection of research papers, projects, and resources focused on the application of agentic AI and AI agents within the healthcare domain. It aims to provide researchers and practitioners with an up-to-date overview of advancements in areas such as medical image analysis, electronic health record (EHR) manipulation, patient dialogue systems, drug discovery, and healthcare administration, thereby accelerating the adoption and development of intelligent healthcare solutions.

How It Works

The project compiles resources detailing AI agents that autonomously perform tasks, make decisions, and interact with their environment. These agents commonly leverage large language models (LLMs), multi-agent systems, and tool integrations. A quantitative analysis of recent literature highlights a strong focus on developing high-level frameworks, enhancing agent reasoning capabilities, and designing multi-agent collaboration paradigms, with textual data remaining the primary modality.

Highlighted Details

• The collection spans diverse healthcare applications including General Medicine, Public Health, Mental Health, Drug Discovery, and Genomics.
• Key technological trends identified include advancements in agent frameworks, reasoning abilities, and multi-agent collaboration.
• Data modalities show a dominance of textual data, with increasing focus on Time-Series and Genomics in recent publications.
• The repository is organized by application categories, such as Doctor-Facing Agents (e.g., Radiology, Pathology, EHR Agents) and Patient-Facing Applications (e.g., Mental Health, Clinical Communication).

Maintenance & Community

This resource is maintained collaboratively by a team from the University of Notre Dame, Johns Hopkins University, and Emory University. The community is encouraged to contribute by opening issues or pull requests for errors or missing papers. A comprehensive survey paper, "A Comprehensive Survey of AI Agents for Healthcare," is available for deeper insights.

Licensing & Compatibility

The repository itself, being a curated list of research papers, does not have a software license. The licensing of individual papers or projects linked within the list would need to be checked separately.

Limitations & Caveats

As a curated list, its completeness and the recency of all included resources are dependent on ongoing community contributions. The rapid pace of AI research means that any such list is a snapshot in time and may not capture the absolute latest developments immediately.