PATHFinder Agent for Tailored Prenatal Care
PATHFinder Agent is an end-to-end conversational system that collects patient health and social context through structured dialogue to create individualized prenatal care plans aligned with ACOG's PATH guidelines, and surfaces community resources from Michigan 211. Evaluation of LLMs found GPT-5.2 achieved the highest average score (77.6%) but identified gaps in antenatal testing recommendations. Future validation through human participant studies and RCTs is planned.
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[Submitted on 9 Jun 2026]
Title:PATHFinder Agent for Tailored Prenatal Care
View a PDF of the paper titled PATHFinder Agent for Tailored Prenatal Care, by Vaibhav Balloli and 4 other authors
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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.
Comments: Accepted as demo at ACM Interactive Health 2026. this https URL
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
Cite as: arXiv:2607.24768 [cs.AI]
(or arXiv:2607.24768v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.24768
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3786579.3804996
DOI(s) linking to related resources
Submission history
From: Vaibhav Balloli [view email] [v1] Tue, 9 Jun 2026 03:40:34 UTC (1,049 KB)
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