Evidence map›Paper›PMID 39915780›Full record

ReviewJournal of biomedical science2025

Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.

Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C Fann

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of biomedical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yi-Ming Chen *Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, 40705, Taiwan.
Tzu-Hung Hsiao *Department of Medical Research, Taichung Veterans General Hospital, Taichung, 40705, Taiwan.
Ching-Heng LinDepartment of Medical Research, Taichung Veterans General Hospital, Taichung, 40705, Taiwan. epid@vghtc.gov.tw.
Yang C FannDivision of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA. fann@ninds.nih.gov.ORCID http://orcid.org/0000-0003-2636-2002

Funding

Academia Sinica 40-05-GMMAcademia Sinica AS-GC-110-MD02National Science and Technology Council NSTC-111-2218-E-039-001National Science and Technology Council NSTC-111-2314-B-075A-003-MY3National Science and Technology Council NSTC -111-2634-F-A49-014Taichung Veterans General Hospital TCVGH-1127304BTaichung Veterans General Hospital TCVGH-1137302BTaichung Veterans General Hospital TCVGH-1137302DTaichung Veterans General Hospital TCVGH-1137310CTaichung Veterans General Hospital TCVGH-1137319C
6 · The paper itself

Abstract

Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, and immunology data. This comprehensive review explores the clinical applications of AI-driven analytics in unlocking personalized insights for patients with autoimmune rheumatic diseases. Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. AI-enabled immunology data analysis, including dimensionality reduction, cell population identification, and sample classification, provides unprecedented insights into complex immune responses. The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. The future of precision medicine lies in the continued research, development, and clinical integration of AI-driven strategies to unlock personalized patient care and drive innovation in rheumatology.

Indexed as

Artificial IntelligencePrecision MedicineHumansArtificial intelligenceAutoimmuneElectronic health recordsGeneticsImmunologyPrecision medicine

Identifiers

PMID39915780
PMCPMC11804102

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.