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

Title:
Conversational Artificial Intelligence for Integrating Social Determinants, Genomics, and Clinical Data in Precision Medicine: Development and Implementation Study of the AI-HOPE-PM System.
Authors:
Yang EW; Polyagent, San Francisco, CA, United States., Waldrup B; Beckman Research Institute, Duarte, CA, United States., Velazquez-Villarreal E; Beckman Research Institute, Duarte, CA, United States.; City Of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA, 91010, United States, 1 6262187162.
Source:
JMIR bioinformatics and biotechnology [JMIR Bioinform Biotechnol] 2025 Oct 10; Vol. 6, pp. e76553. Date of Electronic Publication: 2025 Oct 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101769661 Publication Model: Electronic Cited Medium: Internet ISSN: 2563-3570 (Electronic) Linking ISSN: 25633570 NLM ISO Abbreviation: JMIR Bioinform Biotechnol Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Toronto, ON, Canada : JMIR Publications Inc., [2020]-
References:
N Engl J Med. 2015 Feb 26;372(9):793-5. (PMID: 25635347)
Bioinformatics. 2025 Jul 1;41(7):. (PMID: 40577785)
J Pers Med. 2025 Feb 28;15(3):. (PMID: 40137413)
Cancers (Basel). 2025 Apr 15;17(8):. (PMID: 40282501)
Cancers (Basel). 2024 Feb 29;16(5):. (PMID: 38473374)
Nat Biotechnol. 2020 Jun;38(6):675-678. (PMID: 32444850)
Nat Methods. 2020 Feb;17(2):137-145. (PMID: 31792435)
Nature. 2016 Oct 12;538(7624):161-164. (PMID: 27734877)
NPJ Digit Med. 2025 Mar 25;8(1):178. (PMID: 40133390)
Nature. 2010 Sep 30;467(7315):543-9. (PMID: 20882008)
Cancers (Basel). 2025 Jul 17;17(14):. (PMID: 40723258)
Expert Rev Proteomics. 2004 Jun;1(1):67-75. (PMID: 15966800)
PeerJ. 2025 Feb 10;13:e18895. (PMID: 39950044)
Ann N Y Acad Sci. 2010 Feb;1186:1-4. (PMID: 20201864)
Adv Sci (Weinh). 2024 Nov;11(44):e2407094. (PMID: 39361263)
Int J Mol Sci. 2025 Jul 05;26(13):. (PMID: 40650262)
Neoplasia. 2017 Aug;19(8):649-658. (PMID: 28732212)
Public Health Rep. 2014 Jan-Feb;129 Suppl 2:19-31. (PMID: 24385661)
Front Artif Intell. 2025 Aug 11;8:1624797. (PMID: 40860720)
Cancers (Basel). 2025 Aug 31;17(17):. (PMID: 40940961)
Hum Genomics Proteomics. 2009 Jan 12;2009:. (PMID: 20948564)
Cancer Discov. 2012 May;2(5):401-4. (PMID: 22588877)
Isr Med Assoc J. 2025 Mar;27(3):183-188. (PMID: 40134173)
Cancers (Basel). 2025 Mar 25;17(7):. (PMID: 40227607)
N Engl J Med. 2016 Aug 18;375(7):655-65. (PMID: 27532831)
Nat Rev Genet. 2023 Aug;24(8):550-572. (PMID: 37002403)
Front Oncol. 2024 Dec 03;14:1513821. (PMID: 39711954)
Contemp Oncol (Pozn). 2015;19(1A):A68-77. (PMID: 25691825)
J Cheminform. 2025 Mar 24;17(1):36. (PMID: 40128788)
Biomedicines. 2025 Jul 28;13(8):. (PMID: 40868090)
Commun Biol. 2020 Jun 25;3(1):318. (PMID: 32587328)
Cancer Med. 2025 Apr;14(7):e70791. (PMID: 40165548)
Cancer. 2022 Jan 1;128(1):122-130. (PMID: 34478162)
Contributed Indexing:
Keywords: AI agent; LLM; artificial intelligence; bioinformatics; cancer; genomics; large language model; precision medicine; social determinants of health
Entry Date(s):
Date Created: 20251204 Date Completed: 20251204 Latest Revision: 20251207
Update Code:
20251207
PubMed Central ID:
PMC12513684
DOI:
10.2196/76553
PMID:
41342165
Database:
MEDLINE

Weitere Informationen

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.
(© Ei-Wen Yang, Brigette Waldrup, Enrique Velazquez-Villarreal. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org).)