ReviewPublic health in practice (Oxford, England)2026
Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review.
Review in Public health in practice (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Intersectionality in AI and Machine Learning for Health Care: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- AI-Enabled First-Response Support After Sexual and Gender-Based Violence: A PRISMA-ScR Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review.Public health in practice (Oxford, England) · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study systematically evaluates how existing AI frameworks in healthcare address intersectional bias across the AI lifecycle and explores the mitigation strategies proposed. Study design: Scoping review. Methods: A scoping review was conducted per PRISMA-ScR guidelines, analyzing studies from 2014 to 2024. Searches included MEDLINE (Ovid), PubMed, EMBASE (Ovid), SCOPUS, ESCI, IEEE Xplore, and Google Scholar. Data were extracted on bias-related challenges and mitigation strategies across AI lifecycle phases (development, validation, implementation, monitoring). Studies were ranked by inclusivity (high, medium, or low). Results: Of 374 records, 43 studies met inclusion criteria, primarily from high-income countries. Gender/sex (51.2 %) and race/ethnicity (44.2 %) were the most addressed dimensions, while disability (14 %) and citizenship (9.3 %) were least addressed. Inclusivity was categorized as high (21 studies, 48.8 %), medium (23.2 %), or low (27.9 %). Overall, 14 biases and 21 mitigation strategies were identified. Conclusions: Significant gaps remain in addressing intersectional biases in AI frameworks, particularly for underrepresented groups such as individuals with disabilities and non-citizens. Despite many frameworks demonstrating efforts toward inclusivity, attention to intersectionality remains uneven and largely inconsistent. Mapping biases to lifecycle phases highlights actionable strategies to improve equity and inclusivity in AI-driven healthcare. These findings provide valuable guidance for researchers, policymakers, and developers to create equitable and responsible AI systems.
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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.