Opportunities and challenges of integrating artificial intelligence in focus group discussions for health research in India

Gore, Manisha; Ezebialu, Chioma; Olawade, David

Opportunities and challenges of integrating artificial intelligence in focus group discussions for health research in India

Gore, Manisha, Ezebialu, Chioma and Olawade, David ORCID: https://orcid.org/0000-0003-0188-9836 (2026) Opportunities and challenges of integrating artificial intelligence in focus group discussions for health research in India. Discover Artificial Intelligence, 6 (1). p. 542.

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Official URL: https://doi.org/10.1007/s44163-026-01297-x

Abstract

Background/Aim: Focus Group Discussions (FGDs) are important in qualitative research, particularly for exploring collective perspectives, social behaviors, and cultural norms in health research contexts. While FGDs have proven valuable for capturing community perspectives, the integration of Artificial Intelligence (AI) into FGD methodology presents opportunities and challenges, particularly in diverse settings like India. Despite growing adoption of AI-enhanced qualitative research tools globally, limited synthesis exists on effective and ethical integration into health research in linguistically diverse, resource-constrained contexts. This review examines the methodological integration of AI technologies into FGDs, with specific focus on identifying ethical challenges and implementation barriers in Indian health research contexts. Methods: This narrative review employed thematic synthesis to examine scholarly literature on FGDs and AI integration. Literature searches across PubMed, Scopus, Google Scholar, and ScienceDirect (March–April 2025) covered publications from 1940 to 2024. Using purposive selection, 51 studies were included following screening of 1236 records. Data extraction focused on historical development, methodological frameworks, community health applications, digital innovations, Indian context challenges, and ethical considerations. Results: FGDs evolved from 1940s media research through three phases to become foundational in public health. AI innovations include automated moderation, transcription, sentiment analysis, and coding, offering efficiency and broader reach. Indian context challenges include digital infrastructure disparities (38% rural vs. 82% urban internet access), Natural Language Processing limitations across 22 languages, code-switching complexities, algorithmic bias, and ethical concerns regarding privacy and digital literacy. Conclusion: FGDs remain valuable when combined with emerging technologies and cultural sensitivity. Successful AI integration requires addressing digital equity, developing India-specific NLP tools, ensuring methodological rigor, and implementing ethical safeguards. Hybrid human-AI approaches are recommended over full automation.