Capacity, Collaboration, and Thematic Structure in Health AI Research Over the Past Decade in the Gulf Cooperation Council: Scientometric Analysis.

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Abstract

Health AI investment is accelerating across the Gulf Cooperation Council (GCC), yet the region's health AI research output has never been systematically quantified. National research capacity is a structural precondition for the safe local development and governance of clinical AI, but no scientometric baseline exists for the 6 GCC states. We characterized the volume, growth, collaboration structure, and thematic composition of GCC health AI research and examined whether national output aligns with an existing measure of informatics readiness. We conducted an ecological scientometric analysis of all health AI publications affiliated with the 6 GCC states (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) for 2011 to 2024 (the last complete indexing year at extraction) using the open bibliographic database OpenAlex. A publication was eligible if it included both an AI family and a health or medicine family concept above a 0.30 relevance threshold. Bibliometric indicators (output, compound annual growth, per capita intensity, citation impact, international and intraregional collaboration, and institutional concentration) were computed; coauthorship and concept co-occurrence networks were constructed and clustered by modularity. National health AI intensity was correlated (Spearman) with the Health Informatics Readiness Index (HIRI) and Global Digital Health Monitor (GDHM) phase. Of 112,480 GCC-affiliated AI publications, 3512 met the health AI definition. Regional output rose from 18 (2011) to 904 (2024) publications per year (compound annual growth=35.2%), with 86.4% (3034/3512) published from 2020 onward and a sharp inflection from 2019 to 2021. Saudi Arabia contributed 59.6% (2094/3512) of the output by itself and 81.5% (2862/3512) together with the United Arab Emirates; Qatar led on a per capita basis (approximately 136 publications per million population). International collaboration (2551/3512, 72.6% of the papers) predominated over intra-GCC collaboration (164/3327, 4.9%), with the latter being inversely related to national output. The principal external partners were India, the United States, Pakistan, the United Kingdom, and Egypt. Output spanned 3186 institutions; concentration ranged from 89.4% (328/367) of a country's output among the top 3 institutions for Qatar to 34.4% (721/2094) for Saudi Arabia. Concept co-occurrence was classified into 4 themes-classic machine learning clinical prediction, medical imaging and oncology, COVID-19 response, and diabetic eye disease. Health AI intensity (ρ=+0.60) and, more strongly, absolute output (ρ=+0.89) correlated with the HIRI; absolute output was also correlated with GDHM phase (ρ=+0.83). GCC health AI research has grown rapidly but unevenly, is heavily dependent on international collaboration, is concentrated in medical imaging, and-outside Saudi Arabia-remains anchored in single national institutions. Informatics readiness was associated with both the intensity and the volume of national research output. The findings establish a reproducible baseline for monitoring regional health AI research capacity as GCC health systems move toward clinical AI deployment.

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