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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.