The role of agentic artificial intelligence in healthcare: a scoping review.
Authors
Affiliations (9)
Affiliations (9)
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.
- Department of Radiology AI IT, Mayo Clinic, Rochester, MN, USA.
- Department of Surgery, Mayo Clinic, Jacksonville, FL, USA.
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA.
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA. [email protected].
- Department of Surgery, Mayo Clinic, Jacksonville, FL, USA. [email protected].
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA. [email protected].
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA. [email protected].
Abstract
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 features such as autonomous operation, goal-directed behavior, action initiation, and, in some cases, multi-agent collaboration. Reported outcomes included high accuracy in cancer diagnosis, treatment planning, alert generation, coaching, and workflow optimization. Despite promising results, most studies were exploratory, limited in scope, and lacked robust clinical validation, with only one trial involving patients. These findings highlight both the potential and immaturity of Agentic AI in healthcare, underscoring the need for standardized definitions, regulatory guidance, and rigorous evaluation to ensure safe and effective integration into practice.