Indian research on generative artificial intelligence in healthcare imaging: A comprehensive bibliometric analysis

This study presents a comprehensive bibliometric analysis of Indian research on the application of generative artificial intelligence (GAI) in healthcare imaging from 2017 to 2025. It aims to evaluate the research output, citation impact, collaborative patterns, and key thematic areas to understand India's position in this rapidly evolving global landscape.

We used a comprehensive search strategy on the Scopus database, limited to publications with an Indian affiliation from 2017 to 2025. Data on author names, affiliations, publication years, keywords, and citations were extracted from 383 records. The analysis employed citation analysis, co-authorship networks, and keyword co-occurrence analysis, with VOSviewer software used for data visualization.

Globally, 2,761 papers were published in this field, with an average growth rate of 133.2%. India ranked third globally in publication volume with 383 papers, but its average citations per paper (CPP) were 6.55, much below the global average of 21.71. Conference papers dominated India's output (58.49%) but had a low CPP of 2.92, in contrast to higher-impact journal articles (11.29 CPP). Key institutions such as SRM Institute of Science and Technology were highly productive, while others, such as the GLA University, demonstrated high citation impact. The most prevalent keywords were "generative adversarial networks" and "medical imaging," highlighting a strong focus on technical applications.

Indian research in GAI in healthcare imaging is marked by a significant increase in output, establishing the country as a major contributor. Although India ranks third globally in research output, its citation impact remains below the global average, reflecting the need to improve research quality, visibility, and international collaboration.