Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots
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40900_2025_Article_804.pdf
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Author(s)
Jaiswal, Nikhil
Ma, Yuanchao
Lebouché, Bertrand
Poenaru, Dan
Pomey, Marie-Pascale
Achiche, Sofiane
Lessard, David
Engler, Kim
Montiel, Zully
Acevedo, Hector
Date Issued
December 22, 2025
Journal
Research Involvement and Engagement
Publisher
BioMed Central
Citation
Jaiswal, N., Ma, Y., Lebouché, B. et al. Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots. Res Involv Engagem 11, 143 (2025).
Version
Final published version
Abstract
Uses of large language models (LLMs) in health chatbots are expanding into high-stakes clinical contexts, heightening the need for tools that are evidence-based, accountable, accurate, and patient-centred. This conceptual, practice-informed Perspective reflects on engaging patients and non-academic partners for the responsible integration of LLMs, grounded in the co-construction of MARVIN (for people living with HIV) and in an emerging collaboration with MIT Critical Data. Organised by the Software Development Life Cycle, we describe: conception/needs assessment with patient partners to identify use cases, acceptable trade-offs, and privacy expectations; development that prioritises grounding via vetted sources, structured human feedback, and data-validation committees including patient partners; testing and evaluation using patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) chosen in collaboration with patients to capture usability, acceptability, trust, and perceived safety, alongside task performance and harmful-output monitoring; and implementation via diverse governance boards, knowledge-mobilisation materials to set expectations, and risk-management pathways for potentially unsafe outputs. Based on our experience with MARVIN, we recommend early and continuous engagement of patients and non-academic partners, fair compensation, shared decision-making power, transparent decision logging, and inclusive, adaptable governance that can evolve with changing models and standards. These lessons highlight how patient partnership can directly shape chatbot design and oversight, helping teams align LLM-enabled tools with patient-centred goals while building accountable, safe, and equitable systems.
MIT Department
MIT Critical Data (Laboratory)
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s40900-025-00804-1
