Abstract
Background
There is tremendous enthusiasm for the use of AI in health care because of the ability to analyze existing data for preventative, diagnostic, and treatment support. Agentic AI can feasibly provide access to large real-world datasets for the generation of real-world evidence for health care and clinical applications to health care providers, researchers, and administrators without access to large analytic programming resources.
Objective
The objective of this study was to understand the feasibility of using agentic AI for clinical outcome research and population health management. Specifically, this study used an agentic AI evidence-generation platform to obtain epidemiological estimates of diverse medical conditions, with the results evaluated against existing AI frameworks.
Methods
Prevalence estimates of 6 conditions (amyotrophic lateral sclerosis, acute myeloid leukemia, bladder cancer, Huntington disease, elevated lipoprotein (a), and Parkinson disease) were estimated using an agentic AI evidence-generation platform applied to an administrative claims database with representation from every US state. Gender-specific rates were calculated within the following age categories: 0 to 17, 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65 to 74 years and 75 years and older. Period prevalence was estimated from January 1, 2020, to June 30, 2025, and annual prevalence rates for each year were estimated from 2020 to 2024. Continuous enrollment for 12 months was required during the study period for inclusion. Source code generated by the platform as part of the analysis was reviewed by an independent programmer for validation of methods and programming, and analyses were replicated using traditional programming methods. Results obtained throughout the process were evaluated against several existing AI application frameworks.
Results
Regarding accuracy, epidemiological estimates obtained using the agentic AI platform were consistent with published estimates for all 6 conditions, as well as with estimates obtained from traditional programming methods. Regarding rigor, the agentic AI platform conducted the analysis with rigor by confirming acceptable methods in published literature for the type of data source used. Code lists used for the analysis were confirmed against existing algorithms when available. Appropriate statistical methods were used to compare differences in prevalence rates by age and gender. Regarding trust (explainability, transparency, replicability, traceability, and validation), the agentic AI platform generated all source code used for the analyses, which was reviewed and validated for accuracy and appropriateness. The analysis included a “human in the loop” to validate the research question, data extraction method, statistical analysis plan, and output plan prior to proceeding with each step.
Conclusions
With specific design considerations to ensure responsible use, agentic AI can be invaluable to increasing the accessibility of large datasets for applied clinical outcome research and population health management analyses.