Evidence map›Paper›PMID 42222060›Full record

ArticleIndian journal of psychological medicine2026

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

Deepak S Ghadigaonkar, Diptadhi Mukherjee, Kalyani Bg, Prakrithi Shivaprakash, Tathagata Mahintamani, Bobby Kunhu, Neha B Kulkarni, Vikas Punia, Venkata Lakshmi Narasimha, Lekhansh Shukla

Abstract read
In one paragraph

Article in Indian journal of psychological medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Deepak S GhadigaonkarDept. of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.ORCID https://orcid.org/0000-0001-8635-152X
Diptadhi Mukherjeesup>Dept. of Addiction Medicine, Lokopriya Gopinath Bordoloi Regional Institute of Mental Health (LGBRIMH), Tezpur, Assam, India.ORCID https://orcid.org/0000-0002-0497-2427
Kalyani BgConsultant Psychiatrist, Bengaluru, Karnataka, India.ORCID https://orcid.org/0000-0002-8039-936X
Prakrithi ShivaprakashNational Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.ORCID https://orcid.org/0000-0001-8761-7908
Tathagata Mahintamanisup>Dept. of Addiction Medicine, Lokopriya Gopinath Bordoloi Regional Institute of Mental Health (LGBRIMH), Tezpur, Assam, India.ORCID https://orcid.org/0000-0002-2047-5797
Bobby KunhuIndependent Lawyer, Researcher & Writer, Kerala, India.
Neha B KulkarniTele MANAS, Lokopriya Gopinath Bordoloi Regional Institute of Mental Health (LGBRIMH), Tezpur, Assam, India.ORCID https://orcid.org/0009-0001-3040-8061
Vikas PuniaDept. of Clinical Psychology, Lokopriya Gopinath Bordoloi Regional Institute of Mental Health (LGBRIMH), Tezpur, Assam, India.ORCID https://orcid.org/0000-0002-0031-1373
Venkata Lakshmi NarasimhaCentre for Addiction Medicine, Dept. of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.ORCID https://orcid.org/0000-0001-9111-8714
Lekhansh ShuklaCentre for Addiction Medicine, Dept. of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.ORCID https://orcid.org/0000-0002-4558-3890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The embedding of lived experience experts (LEEs) is increasingly recognized as important in research, but evidence from low- and middle-income countries (LMIC) remains sparse. This viewpoint aims to present and critically reflect on a model for integrating LEEs into the design of an artificial intelligence (AI)-based mental health (MH) study to develop an automated depression screening system in India. We report on the process of forming a diverse Lived Experience Advisory Panel (LEAP) and show how LEAP influenced methodology, system design, ethics, and language through a three-day workshop and co-design sessions. We also highlight the ethical, structural, and power-dynamic challenges encountered, demonstrating that meaningful LEE partnership is both feasible and essential for developing culturally grounded, safe, and clinically acceptable AI applications in LMIC settings.

Indexed as

Artificial intelligencedepressionlived experience expertlow- and middle-income countriesmental health

Identifiers

PMID42222060
PMCPMC13221382

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.