Evidence map›Paper›PMID 41595426›Full record

ReviewGenes2025

The Evolving Role of Artificial Intelligence in Medical Genetics: Advancing Healthcare, Research, and Biosafety Management.

Ying-Cheng Wu, Nan Tuo, Guoming Shi, Ka Li, Zhenju Song, Yanying Li

Abstract readReview
In one paragraph

Review in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics.Heart views : the official journal of the Gulf Heart Association · 2026
    Review
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

6 authors.

Ying-Cheng WuShanghai Medical College, Fudan University, Shanghai 200032, China.ORCID 0000-0001-9473-546X
Nan TuoClinical Research Centre, The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Guoming ShiDepartment of Research, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Ka LiDepartment of Research, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Zhenju SongDepartment of Research, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Yanying LiDepartment of Research, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Funding

Fudan University Hospital Management Construction Key Project FDYGC20230101Shanghai Municipal Health Commission Clinical Research Special Project W2023-014
6 · The paper itself

Abstract

The integration of artificial intelligence (AI) with medical genetics is transforming healthcare by addressing the analytical challenges posed by the vast complexity of multi-omics data. This review explores the synergistic convergence of these fields, highlighting AI's transformative role in enhancing diagnostic precision, enabling non-invasive molecular profiling through imaging-genetics, and advancing predictive and personalized medicine via polygenic risk scores and pharmacogenomics. AI is also emerging as a powerful generative tool in therapeutic design, accelerating drug discovery, protein engineering, and precision gene editing. However, this powerful synergy introduces significant ethical, regulatory, and biosecurity challenges, including data privacy, algorithmic bias, and the dual-use risks of AI-enabled genetic engineering. The future envisions a responsible co-evolution, with multimodal AI and the concept of the Digital Twin driving precision medicine, underpinned by interdisciplinary collaboration to ensure fairness, transparency, and societal trust. This article charts the current landscape and proposes actionable directions, emphasizing the need for robust governance to harness AI's potential while mitigating its risks for the benefit of human health.

Indexed as

Artificial IntelligenceGenetics, MedicalDelivery of Health CareHumansPrecision Medicineartificial intelligencebiosecuritydigital twinmedical geneticspharmacogenomics

Identifiers

PMID41595426
PMCPMC12840862

What Socratic holds

Textmetadata
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 Socratic graph.