PATHFinder Agent for Tailored Prenatal Care
Quick Answer
The PATHFinder Agent is an AI-driven system that personalizes prenatal care by collecting patient data and generating tailored healthcare plans based on ACOG's PATH guidelines.
Quick Take
Evaluated against , GPT-5.2 scored the highest at 77.6%, highlighting gaps in antenatal testing recommendations. Future studies will validate its effectiveness through human trials.
Key Points
- PATHFinder Agent uses structured dialogue for patient health data collection.
- The system generates individualized prenatal care plans aligned with PATH guidelines.
- GPT-5.2 achieved a 77.6% average score in clinical evaluations.
- Identified gaps in antenatal testing recommendations during evaluations.
- Future validation will involve human participant studies and randomized trials.
DeepSignal Analysis
What happened
The PATHFinder Agent is an AI system designed to personalize prenatal care by utilizing patient data to create individualized healthcare plans based on ACOG's PATH guidelines. The system operates through a structured four-stage workflow and has been evaluated against large language models, with GPT-5.2 achieving the highest score of 77.6%. Future studies are planned to assess its effectiveness in real-world settings.
Key evidence
- The PATHFinder Agent collects patient health and social context through structured dialogue and generates tailored prenatal care plans.
- GPT-5.2 scored 77.6% in evaluations against large language models, indicating its performance in generating antenatal testing recommendations.
- Future validation of the PATHFinder Agent's effectiveness will occur through human participant studies and randomized controlled trials.
Why it matters
Personalized prenatal care can significantly improve health outcomes for pregnant individuals by addressing their unique health and social needs. The introduction of the PATHFinder Agent aligns with recent guidelines from ACOG, which emphasize tailored healthcare. Evaluating its performance against leading AI models highlights both its potential and existing gaps in antenatal care recommendations, underscoring the need for further research.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 |
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| 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)
— Originally published at arxiv.org
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