Evidence map›Paper›PMID 41209363›Full record

ReviewScience in One Health2025

AI tools accelerating One Health advancement: insights from the Global Public Health Week 2025 Webinar.

Jingshu Liu, Shenning Lu, Jiming Liu, Huaiyu Tian, Md Tanvir Rahman, Issam Bennis, Somphou Sayasone, Daniela Polessa, Khaja Mohteshamuddin, Quanle Li and 5 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Science in One Health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Jingshu LiuSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Shenning LuNational Institute of Parasitic Diseases at China CDC (Chinese Center for Tropical Diseases Research), WHO Collaborating Centre for Tropical Diseases, Shanghai 200025, China.
Jiming LiuHong Kong Baptist University, Hong Kong 999077, China.
Huaiyu TianBeijing Normal University, Beijing 100875, China.
Md Tanvir RahmanDepartment of Microbiology and Hygiene, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.
Issam BennisMohammed VI University of Sciences and Health, Casablanca 82403, Morocco.
Somphou SayasoneLaos Tropical and Public Health Institute, Ministry of Health, Vientiane Capital 01030, Lao PDR.
Daniela PolessaUniversity of Rio de Janeiro, Rio de Janeiro CEP 21941-901, Brazil.
Khaja MohteshamuddinUnited Arab Emirates University, Abu Dhabi 15551, United Arab Emirates.
Quanle LiChinese Preventive Medicine Association, Beijing 102206, China.
Heya YiChinese Preventive Medicine Association, Beijing 102206, China.
Mayumi WakimotoOswaldo Cruz Foundation, Rio de Janeiro CEP 21040-900, Brazil.
Arve Lee WillinghamUnited Arab Emirates University, Abu Dhabi 15551, United Arab Emirates.
Raman BediKing's College London, London WC2R 2LS, United Kingdom.
Xiao-Nong ZhouSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This commentary emerges from a webinar titled "AI Tools Accelerating One Health Advancement for a Public Health Perspective," held on World Health Day 2025. The event convened global experts to explore how artificial intelligence (AI) reshapes One Health initiatives. Key discussions addressed AI applications in data integration, risk assessment, and real-time public health responses. Major outcomes included strategies for incorporating AI into public health research, fostering multi-sectoral partnerships, addressing ethical and privacy concerns, and improving AI literacy. This commentary proposes six core recommendations: (1) establish robust governance frameworks; (2) develop a global AI ethics charter; (3) mandate transparency in training data and algorithms; (4) expand open-data access and AI literacy programs; (5) create interdisciplinary One Health innovation hubs; and (6) ensure AI solutions are both cutting-edge and equitable. These proposals aim to guide responsible and inclusive AI implementation to safeguard human, animal, and environmental health.

Indexed as

Artificial intelligenceCapacity buildingData integrationEthicsGovernanceOne HealthPublic healthRisk assessment

Identifiers

PMID41209363
PMCPMC12595101

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.