AI in healthcare: mapping Romania's transition from research to intellectual property & industry solutions

Clinical Snapshot

Key Findings

• P Value: Not applicable - descriptive study

• Effect Size: 619 Romanian-authored medical AI articles, 272 patent records, 6 identified AI medical software solutions

• Primary Outcome: Romania shows growing medical AI research output but limited translation to patents and commercial solutions

• Confidence Interval: Not provided

Clinical Application

Limited direct clinical applicability - primarily policy and research planning relevance Limited relevance to Australian practice given different regulatory environment (TGA vs EU AI Act) and more mature AI healthcare ecosystem Healthcare policymakers and AI researchers in similar emerging economies

Abstract

OBJECTIVES: Romania is aligning its healthcare AI ecosystem with the European Union's AI Act (2024/1689), through its own National AI Strategy. Despite a growing volume of research in medical AI, significant gaps remain in translating this output into patents and domestically developed high-risk medical devices. This study assesses Romania's research productivity, patenting activity, and commercial solutions in medical AI. METHODS: We conducted an analysis combining bibliometric data, patent information, and medical device databases, and analyzed 619 Romanian-authored articles on medical AI, 272 patent records, and identified domestic AI-powered software providers. RESULTS: Romania's publication output in medical AI has surged post-2018, primarily driven by academic institutions in Bucharest and Cluj. Patents are predominantly filed by multinationals, indicating a potential disconnect between research and industrial output. Six AI medical software solutions were identified, most under the low-risk classifications. CONCLUSIONS: While Romania is expanding its medical AI research, it faces barriers in converting academic output into innovation. The dominance of foreign corporations in patent filings, reliance on international funding for high-impact research, and the scarcity of domestically developed high-risk medical AI solutions highlight important gaps. Addressing these disparities is essential for national alignment in the AI medical innovation space.