Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Health Services Insights

DOI: 10.1177/11786329261477136

Authors: Riley Martens, Linda Bakunda, Teresa Mushayandebvu, Zack Marshall

Institutions: University of Calgary, McGill University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,000