PATHFinder Agent for Tailored Prenatal Care

Abstract:Prenatal care is an important preventive service designed to improve outcomes for pregnant individuals. The American College of Obstetricians and Gynecologists (ACOG) recently introduced guidelines advocating tailored prenatal care, called PATH (Plan for Tailored Healthcare). We present PATHFinder Agent(Planner for Appropriate Tailored Healthcare), an end-to-end conversational agentic system that gathers patient health and social context through structured dialogue, curates individualized prenatal care plans aligned with PATH guidelines, and surfaces community resources from Michigan 211. The system features a four-stage workflow spanning patient intake, dynamic interaction, plan synthesis, and clinician oversight. We evaluate frontier large language models (LLMs) on expert-curated rubrics across five clinical dimensions, finding that GPT-5.2 achieves the highest average score (77.6\%) while identifying key gaps in antenatal testing recommendations. We discuss future validation through human participant studies and randomized controlled trials.

Accepted as demo at ACM Interactive Health 2026. this https URL

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Emerging Technologies (cs.ET)

arXiv:2607.24768 [cs.AI]

(or arXiv:2607.24768v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.24768 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3786579.3804996 Focus to learn more DOI(s) linking to related resources

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