The promise and peril of AI for disability health equity: A justice-centered narrative review
- 1Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- 2Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- 3HES-SO University of Applied Arts Sciences and Arts of Western Switzerland, Geneva, Switzerland
Abstract
Despite the promising potential of Artificial Intelligence (AI) models to enhance health equities for persons with disabilities, poorly designed applications risk exacerbating inequities. To address this, we conducted a narrative review using disability justice frameworks as an analytical lens to evaluate how AI applications operationalize disability and health equity across various domains. AI models demonstrate potential in early intervention, personalized care, equitable resource allocation, assistive technologies, and health surveillance. However, most AI models rely primarily on biomedical and functional data, often trained on biased datasets that neglect social and structural determinants of disability. As a result, these applications insufficiently capture broader dimensions of wellbeing, including capabilities, recognition, and structural justice, limiting their effectiveness in addressing health inequities. To advance health equity for persons with disabilities, AI applications must incorporate disability-inclusive datasets, participatory co-design with persons with disabilities, auditing for fairness, and real-world validation. Integrating clinical, social, and structural dimensions in a justice-oriented framework can help AI models move beyond narrow biomedical models toward more inclusive, context-sensitive, and equitable health systems.
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BibTeX
@article{dalal2026promise,
title = {{The promise and peril of AI for disability health equity: A justice-centered narrative review}},
author = {Jyoti Dalal and Douglas Teodoro and Minerva Rivas Velarde},
journal = {PLOS Digital Health},
year = {2026},
month = sep,
doi = {10.1371/journal.pdig.0001663},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001663},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The promise and peril of AI for disability health equity: A justice-centered narrative review
AU - Jyoti Dalal
AU - Douglas Teodoro
AU - Minerva Rivas Velarde
PY - 2026
DA - 2026/09/01
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001663
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001663
AB - Despite the promising potential of Artificial Intelligence (AI) models to enhance health equities for persons with disabilities, poorly designed applications risk exacerbating inequities. To address this, we conducted a narrative review using disability justice frameworks as an analytical lens to evaluate how AI applications operationalize disability and health equity across various domains. AI models demonstrate potential in early intervention, personalized care, equitable resource allocation, assistive technologies, and health surveillance. However, most AI models rely primarily on biomedical and functional data, often trained on biased datasets that neglect social and structural determinants of disability. As a result, these applications insufficiently capture broader dimensions of wellbeing, including capabilities, recognition, and structural justice, limiting their effectiveness in addressing health inequities. To advance health equity for persons with disabilities, AI applications must incorporate disability-inclusive datasets, participatory co-design with persons with disabilities, auditing for fairness, and real-world validation. Integrating clinical, social, and structural dimensions in a justice-oriented framework can help AI models move beyond narrow biomedical models toward more inclusive, context-sensitive, and equitable health systems.
SN - 2767-3170
ER -