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Integrating Lived Experience Perspectives in AI-based Mental Health Research: A Practice-based Reflective Account.

Deepak S Ghadigaonkar¹, Diptadhi Mukherjee², Kalyani Bg³

¹Dept. of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.

Indian Journal of Psychological Medicine

|June 1, 2026

Summary

Integrating lived experience experts (LEEs) into artificial intelligence (AI) mental health (MH) research in low-income countries is crucial. This study demonstrates a feasible model for developing culturally relevant AI depression screening tools in India through meaningful LEE partnership.

Keywords:

Artificial intelligence depression lived experience expert low- and middle-income countries mental health

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Area of Science:

Mental Health Research
Artificial Intelligence Applications
Global Health Equity

Background:

Lived experience experts (LEEs) are vital in research, yet their involvement in low- and middle-income countries (LMIC) is underrepresented.
Developing AI-based mental health (MH) tools requires culturally sensitive approaches, especially in diverse LMIC settings.

Purpose of the Study:

To present and critically reflect on a model for integrating LEEs in the design of an AI-based MH study in India.
To develop an automated depression screening system using artificial intelligence.
To explore the influence and challenges of LEE partnership in LMIC research.

Main Methods:

Formation of a diverse Lived Experience Advisory Panel (LEAP).
Utilizing a three-day workshop and co-design sessions for LEE integration.
Documenting LEAP's influence on methodology, system design, ethics, and language.

Main Results:

The Lived Experience Advisory Panel significantly influenced study design, ethics, and language.
Identified ethical, structural, and power-dynamic challenges in establishing LEE partnerships.
Demonstrated the feasibility and essential nature of LEE involvement in AI MH research.

Conclusions:

Meaningful partnership with LEEs is essential for creating culturally grounded, safe, and clinically acceptable AI MH applications in LMIC.
The presented model offers a pathway for effective LEE integration in AI research within LMIC contexts.
Addressing challenges is key to successful collaboration and equitable AI development.