PATHFinder Agent for Tailored Prenatal Care
For prenatal care providers, this work presents a prototype system to automate tailored care planning per new guidelines, but it is an early-stage proof-of-concept with limited validation.
The paper introduces PATHFinder Agent, an end-to-end conversational system that gathers patient context through dialogue and generates individualized prenatal care plans aligned with ACOG's PATH guidelines. Evaluation of frontier LLMs shows GPT-5.2 achieves 77.6% average score on expert rubrics, revealing gaps in antenatal testing recommendations.
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.