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Operationalising Inclusion for Participatory Design: Worked Examples for TRIPOD+AI & PROBAST+AI.

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the healthcare landscape, enhancing diagnostic accuracy, personalising treatments, and improving operational efficiency. However, alongside these advancements comes a critical concern: the potential for AI models to perp...

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Published in:Health Services Insights Vol. 19; pp. 1 - 12
Main Authors: Martens, Riley, Bakunda, Linda, Mushayandebvu, Teresa, Marshall, Zack
Format: tables/charts Journal Article
Published: Sage Publications Inc. 8/19/2026
Online Access:View this record in EBSCOhost
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      dt: 8/19/2026
      vid: 19
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Operationalising Inclusion for Participatory Design: Worked Examples for TRIPOD+AI & PROBAST+AI.
      aug:
        au:
          Martens, Riley
          Bakunda, Linda
          Mushayandebvu, Teresa
          Marshall, Zack
        affil: Department of Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, AB, Canada
      sug:
        subj:
          Diversity, Equity, Inclusion
          Research, Medical
          Action Research
          Study Design
          Artificial Intelligence
          Health Care Delivery
          Prediction Models
          Research Subject Recruitment
          Machine Learning
          Length of Stay
          Research Ethics
          Patient Participation
          Computer Literacy
          Information Literacy
          Health Information
          Decision Support Systems, Clinical
          Mortality
          Transfer, Discharge
          Health Services Research
          Diagnostic Services
          Monitoring, Physiologic
          Drug Discovery
          Research Priorities
          Research Support
          Trust
          Precision
          Research, Interdisciplinary
          Accountability
      ab: Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the healthcare landscape, enhancing diagnostic accuracy, personalising treatments, and improving operational efficiency. However, alongside these advancements comes a critical concern: the potential for AI models to perpetuate, or even exacerbate, health inequities. This paper examines the complex potential of AI in health, and how it can both improve and undermine equity. The key to addressing these challenges lies not only in developing advanced algorithms but in fostering responsible and inclusive development. We argue that to operationalise inclusion in AI systems, research co-design should be prioritised in order to integrate the perspectives of diverse knowledge users, including patients, clinicians, and community partners, shown through worked examples in TRIPOD+AI and PROBAST+AI. This approach encourages future work to rethink the role of knowledge users in the development of AI for healthcare.
      pubtype: Academic Journal
      doctype:
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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