aceperformance and maturity of agentic AI systems for clinical tasks in healthcare
Source article: The role of agentic artificial intelligence in healthcare: a scoping review
Agentic AI represents a promising evolution of artificial intelligence in healthcare, with systems capable of operating autonomously to achieve defined clinical goals. However, the literature lacks conceptual clarity in distinguishing AI agents from Agentic AI, and few studies have rigorously explored their applications. We conducted a scoping review across five databases, identifying seven eligible studies spanning emergency medicine, oncology, radiology, and rehabilitation. The included systems demonstrated fe…
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"University Hospital Birmingham - Queen Elizabeth Hospital - Cancer Research UK -The University of Birmingham Institute for Cancer Studies" by ell brown is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.
A March 2026 scoping review in npj Digital Medicine examined agentic AI in healthcare, defined as systems capable of operating autonomously to achieve defined clinical goals. Across five databases, seven studies met criteria, spanning emergency medicine, oncology, radiology, and rehabilitation, with features including autonomous operation, goal-directed behavior, action initiation, and multi-agent collaboration.
The review matters because it separates early technical promise from clinical readiness. While pilots reported high accuracy in diagnosis, treatment planning, and workflow optimization, the authors found conceptual confusion and a lack of rigorous evaluation, with only one patient-involving trial, indicating that safe integration will require clearer definitions, regulatory guidance, and stronger validation.
- Scoping review across five databases identified only seven eligible studies of agentic AI in healthcare.
- Included systems showed autonomous operation, goal-directed behavior, action initiation, and in some cases multi-agent collaboration.
- Applications spanned emergency medicine, oncology, radiology, and rehabilitation.
- Authors noted conceptual confusion between AI agents and Agentic AI in the literature.
Agentic AI systems demonstrated autonomous, goal-directed behavior with high accuracy in cancer diagnosis, treatment planning, alert generation, coaching, and workflow optimization across emergency medicine, oncology, radiology, and rehabilitation pilots.
Most agentic AI studies in healthcare were exploratory, limited in scope, lacked robust clinical validation, and lacked conceptual clarity, with only one trial involving patients.
The rundown
The review searched five databases and included seven studies covering emergency medicine, oncology, radiology, and rehabilitation, with systems exhibiting autonomous operation and goal-directed behavior.
Despite reported high accuracy in tasks like cancer diagnosis and treatment planning, the evidence base was immature, with exploratory designs, limited scope, and minimal patient involvement, prompting calls for standardized definitions and regulatory guidance.
Most included studies were exploratory and lacked robust clinical validation, with only one trial involving patients, limiting generalizability to real-world practice.
Sources
- Peer-reviewednpj Digital Medicine2026-03-14
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