Conversational Artificial Intelligence for Integrating Social Determinants, Genomics, and Clinical Data in Precision Medicine: Development and Implementation Study of the AI-HOPE-PM System

Abstract

Background: Integrating clinical, genomic, and social determinants of health (SDOH) data is essential for advancing precision medicine and addressing cancer health disparities. However, existing bioinformatics tools often lack the flexibility to perform equity-driven analyses or require significant programming expertise. Objective: We developed AI-HOPE-PM (Artificial Intelligence Agent for High-Optimization and Precision Medicine in Population Metrics), a conversational artificial intelligence system designed to enable natural language-driven, multidimensional cancer analysis. This study describes the development, implementation, and application of AI-HOPE-PM to support hypothesis testing that integrates genomic, clinical, and SDOH data. Methods: AI-HOPE-PM leverages large language models and Python-based statistical scripts to convert user-defined natural language queries into executable workflows. It was evaluated using curated colorectal cancer datasets from The Cancer Genome Atlas and cBioPortal, enriched with harmonized SDOH variables. Accuracy of natural language interpretation, run time efficiency, and usability were benchmarked against cBioPortal and UCSC Xena. Results: AI-HOPE-PM successfully supported case-control stratification, survival modeling, and odds ratio analysis using natural language prompts. In colorectal cancer case studies, the system revealed significant disparities in progression-free survival and treatment access based on financial strain, health care access, food insecurity, and social support, demonstrating the importance of integrating SDOH in cancer research. Benchmark testing showed faster task execution compared to existing platforms, and the system achieved 92.5% accuracy in parsing biomedical queries. Conclusions: AI-HOPE-PM lowers technical barriers to integrative cancer research by enabling real-time, user-friendly exploration of clinical, genomic, and SDOH data. It expands on prior work by incorporating equity metrics into precision oncology workflows and offers a scalable tool for supporting disparities-focused translational research. Five videos are included as multimedia appendices to demonstrate platform functionality in real-world scenarios.

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