Evidence map›Paper›PMID 41243050›Full record

ReviewCurrent environmental health reports2025

Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.

Dongyang Han, Yanyi Xu, Luofei Lin, Xia Meng, Renjie Chen, Haidong Kan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current environmental health reports, 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. Article
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.

Dongyang HanSchool of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, 200032, China.
Yanyi XuSchool of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, 200032, China.
Luofei LinSchool of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China.
Xia MengSchool of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, 200032, China.
Renjie ChenSchool of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, 200032, China. chenrenjie@fudan.edu.cn.
Haidong KanSchool of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, 200032, China. kanh@fudan.edu.cn.

Funding

National Natural Science Foundation of China 82430105
6 · The paper itself

Abstract

The rapid advancement of artificial intelligence (AI) presents unprecedented opportunities and challenges for assessing planetary health, particularly in environmental health. As a key determinant of human well-being, the environment significantly influences health. Although the application of AI in these areas has garnered increasing attention, a comprehensive evaluation framework is still lacking. In this review, we bridge this gap by proposing a unified evaluation framework that spans the entire environmental health research continuum, from modeling environmental exposures to assessing health outcomes and inferring causal relationships. We synthesize recent methodological innovations, application scenarios, and emerging trends across these interconnected domains. Our work highlights how AI can enhance accuracy, scalability, and causal understanding in environmental health studies. By emphasizing this integrated perspective, this review underscores AI's synergistic potential in addressing complex environmental health challenges and informing planetary health strategies.

Indexed as

Artificial IntelligenceEnvironmentEnvironmental HealthEnvironmental ExposureGlobal HealthHumansArtificial intelligenceBig dataEnvironmental healthExposure assessmentMachine learning

Identifiers

PMID41243050

What Socratic holds

Textmetadata
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.