AI-HOPE: An AI-Driven conversational agent for enhanced clinical and genomic data integration in precision medicine research

Abstract

Abstract Introduction: The increasing complexity of clinical cancer research necessitates the development of automated tools capable of integrating clinical and genomic data while accelerating discovery efforts. Artificial Intelligence agent for High-Optimization and Precision mEdicine (AI-HOPE) is introduced as an innovative conversational AI platform powered by Large Language Models (LLMs), designed to empower domain experts to perform integrative data analyses through natural language input, eliminating the need for programming expertise. AI-HOPE offers robust analytical capabilities, enabling the generation of actionable insights in clinical and translational research. Methods: AI-HOPE interprets user instructions in natural language and translates them into executable code to analyze locally stored data. It facilitates subset comparisons for clinical prevalence and survival analysis, generating statistical outputs such as odds ratios, Kaplan-Meier survival curves, and hazard ratios. Its capabilities were demonstrated through two case-control studies using The Cancer Genome Atlas (TCGA): (1) analyzing TP53 mutation enrichment in early-stage versus late-stage colorectal cancer (CRC) patients, and (2) comparing progression-free survival among FOLFOX-treated patients with or without RAS mutations. Results: In the first study, AI-HOPE identified a significant enrichment of TP53 mutations in late-stage (III/IV) CRC compared to early-stage (I/II) cases. In the second study, AI-HOPE revealed a significant association between KRAS mutations and poorer progression-free survival in FOLFOX-treated patients. These findings align with established literature, demonstrating AI-HOPE’s capability to independently uncover meaningful insights without prior user assumptions. Conclusions: AI-HOPE represents a transformative advancement in precision medicine research, offering a scalable, user-friendly framework for integrating clinical and genomic data. Its versatility extends beyond cancer research, supporting applications across diverse biomedical fields. Future enhancements, such as real-time data integration and multi-omics capabilities, will further solidify its role as a pivotal resource for advancing translational research and improving patient outcomes. AI-HOPE bridges the gap between data complexity and research needs, accelerating discoveries in precision medicine research.

Ähnliche Arbeiten