Agentic AI in Healthcare: Opportunities, Challenges, and Future Directions
- 1Real World Solutions, IQVIA
Abstract
The integration of Agentic AI into healthcare marks a new era in medical automation and decision support. Leveraging autonomous reasoning, planning, and collaboration, these AI systems are transforming clinical workflows, diagnostics, and patient management. Recent advancements have led Agentic AI to evolve from single-agent decision-making to multi-agent systems, enabling more sophisticated problem-solving in complex medical environments. In this survey, we provide an in-depth discussion on the core aspects and challenges of Agentic AI in healthcare, including its applications in electronic health records (EHR) interactions, clinical triage, medical question-answering, and disease diagnosis. We categorize existing single-agent and multi-agent frameworks, explore key evaluation metrics, implementation strategies, and commonly used benchmarks, and examine the communication, reasoning, and decision-making processes of these AI agents. Additionally, we address critical challenges such as model reliability, interoperability, ethical considerations, and regulatory constraints. To support further research, we summarize relevant datasets and benchmarks and we outline future research directions that emphasize human-AI collaboration, transparency, and the safe deployment of AI-driven medical systems.
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BibTeX
@article{gridach2026agentic,
title = {{Agentic AI in Healthcare: Opportunities, Challenges, and Future Directions}},
author = {Mourad Gridach and Jay Nanavati and Khaldoun Zine El Abidine and Calum Yacoubian and Christina DeFilippo Mack},
journal = {ACM Computing Surveys},
year = {2026},
month = may,
doi = {10.1145/3809164},
url = {https://doi.org/10.1145/3809164},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Agentic AI in Healthcare: Opportunities, Challenges, and Future Directions
AU - Mourad Gridach
AU - Jay Nanavati
AU - Khaldoun Zine El Abidine
AU - Calum Yacoubian
AU - Christina DeFilippo Mack
PY - 2026
DA - 2026/05/19
JO - ACM Computing Surveys
DO - 10.1145/3809164
UR - https://doi.org/10.1145/3809164
AB - The integration of Agentic AI into healthcare marks a new era in medical automation and decision support. Leveraging autonomous reasoning, planning, and collaboration, these AI systems are transforming clinical workflows, diagnostics, and patient management. Recent advancements have led Agentic AI to evolve from single-agent decision-making to multi-agent systems, enabling more sophisticated problem-solving in complex medical environments. In this survey, we provide an in-depth discussion on the core aspects and challenges of Agentic AI in healthcare, including its applications in electronic health records (EHR) interactions, clinical triage, medical question-answering, and disease diagnosis. We categorize existing single-agent and multi-agent frameworks, explore key evaluation metrics, implementation strategies, and commonly used benchmarks, and examine the communication, reasoning, and decision-making processes of these AI agents. Additionally, we address critical challenges such as model reliability, interoperability, ethical considerations, and regulatory constraints. To support further research, we summarize relevant datasets and benchmarks and we outline future research directions that emphasize human-AI collaboration, transparency, and the safe deployment of AI-driven medical systems.
SN - 0360-0300
SN - 1557-7341
ER -