ReviewEBioMedicine2026
Integrating explainable AI and One Health: a new frontier in combating infectious diseases.
Review in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- From Reactive to Predictive One Health: AI-Enabled Frameworks for Integrated Zoonotic Surveillance and Governance.International journal of environmental research and public health · 2026Article
- Application of Artificial Intelligence in analysing meteorological data for health research: A rapid review.Air quality, atmosphere, & health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases (IDs) remain a major threat to global health and societal stability. Because most emerging IDs in humans are zoonotic in origin and shaped by environmental contexts, effective prevention and control call for a One Health approach. Machine learning is widely used for ID modelling and forecasting but often lacks interpretability to explain predictions or guide public health action. Explainable AI (XAI) makes complex models interpretable, enabling attribution of predictions and identification of key outbreak drivers. In this Personal View, we argue that embedding XAI within a One Health framework offers a new organising principle for ID intelligence. We highlight emerging applications in surveillance and forecasting, zoonotic spillover, antimicrobial resistance monitoring and optimisation of resource allocation. We also outline key challenges, including data harmonisation, governance, privacy protection and equitable distribution of risks and benefits. Advancing XAI-enabled One Health systems will require collaboration across sectors and methodological innovation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.