Abstract 1115: PM-AI agent: A conversational artificial intelligence system for precision medicine and advancing health equity through integrative clinical, genomic and social determinants of health data analysis
Authors: Enrique I. Velazquez-Villarreal, Ei-Wen Yang
Journal: Cancer Research (2025)

Abstract Introduction: The integration of clinical, genomic, and social determinants of health (SDoH) data is essential for advancing precision medicine. However, significant challenges remain due to the complexity of cancer research workflows, the technical expertise required, and the pressing need to address cancer health disparities. To overcome these barriers, we developed the Precision Medicine AI Agent (PM-AI)—an innovative conversational AI system that enables users to conduct integrative data analyses through natural language inquiries. Methods: PM-AI leverages large language models (LLMs) to convert user instructions into executable code and autonomously analyze local stored datasets. The system performs advanced tasks such as subset comparisons, clinical prevalence studies using odds ratio tests, and survival analyses that provide survival curves and hazard ratios. Additionally, PM-AI delivers detailed analytic reports with actionable insights to support decision-making. To validate its utility, PM-AI was applied to a case-control study using data from TCGA and cBioPortal (including the AACR GENIE dataset). The study examined RAS mutations and their impact on the response to FOLFOX (folinic acid, 5-fluorouracil, and oxaliplatin) in colorectal cancer patients. Notably, the analysis incorporated race, ethnicity, and SDoH data, adhering to NASEM recommendations for population descriptors in genomics research, ensuring equitable and representative insights. Results: PM-AI successfully compared FOLFOX-treated patients with or without RAS mutations and revealed a significant difference in progression-free survival. The result show that KRAS mutations predict a poorer response to FOLFOX and an increased risk of recurrence. These findings were consistent with those reported in the literature, validating PM-AI’s independent analytical capabilities. Conclusion: The development of PM-AI represents a transformative advancement in precision medicine research, addressing key challenges in the integration of clinical, genomic, population-specific variables (race, ethnicity, etc.), and SDoH, into analytical pipelines. By eliminating the need for programming, PM-AI bridges domain expertise with autonomous workflows, empowering researchers across disciplines. Its versatility in handling cross-domain datasets highlights its adaptability to diverse precision medicine applications. Overall, PM-AI ensures equitable and actionable insights, positioning this advance AI system as a scalable tool for advancing precision medicine and promoting health equity in cancer research. Citation Format: Enrique I. Velazquez-Villarreal, Ei-Wen Yang. PM-AI agent: A conversational artificial intelligence system for precision medicine and advancing health equity through integrative clinical, genomic and social determinants of health data analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1115.