ReviewAntimicrobial stewardship & healthcare epidemiology : ASHE2026
Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.
Review in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Artificial intelligence (AI) and machine learning (ML) have the potential to improve diagnostic accuracy, infection control protocols, and health outcomes at the population level. AI/ML also has the potential to exacerbate existing health disparities, underscoring the necessity of developing, testing, and evaluating models with equity-centered frameworks. In this review, we highlight current work and future directions in implementing AI/ML to improve health equity in healthcare epidemiology. AI/ML models can improve the quality of data collected in electronic health records, especially with regard to race and ethnicity, aid in identifying vulnerable populations via classification modeling, and help guide equitable intervention protocols by identifying disparities through predictive modeling. However, data-, algorithm-, and deployment-centric biases must be considered at every step of model synthesis, utilizing fairness metrics to assess the presence of bias and employing ideal mitigation strategies to create equitable AI/ML models for healthcare epidemiology.
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.