20.500.12556/DKUM-97008 Symbiosis in health the powerful alliance of AI and propensity score matching in real world medical data analysis The rapid expansion of real-world medical data is driving a transformative shift toward integrating artificial intelligence (AI) with propensity score matching (PSM) to enhance clinical research. While AI provides advanced capabilities in diagnostics and prediction, PSM serves as a critical statistical tool for mitigating confounding bias in quasi-experimental studies, thereby approximating the reliability of randomized controlled trials. This study utilized synthetic thematic analysis (STA) and bibliometric mapping via VOSviewer and Bibliometrix to analyze 433 documents retrieved from the Scopus database. The findings reveal an exponential growth in this field between 2020 and 2024, with the United States and China emerging as the primary contributors to global research output. Four central thematic clusters were identified: prediction, cancer management, diagnostics, and deep learning. The integration is bidirectional, characterized by AI algorithms optimizing propensity score estimation and PSM frameworks being used to enhance AI-driven models. This methodological convergence is significantly improving the rigour of observational studies, particularly in complex clinical domains such as cardiovascular disease and chronic illness management. Ultimately, the AI-PSM symbiosis represents a critical trend in medical informatics, refining the accuracy of predictive modelling and strengthening the evidentiary value of real-world data in global health research. propensity score matching artificial intelligence real world decision making synthetic thematic analysis research landscapes umetna inteligenca sprejemanje odločitev medicinski podatki true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/applsci © 2026 by the authors Članek v reviji 2026-02-11 14:22:08 2026-02-11 14:22:08 2026-02-12 03:14:19 0000-00-00 00:00:00 2026 0 0 21 str. iss. 3, [article no.] 1524 Vol. 16 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-12-01 2026-01-30 2026-02-03 004.8 267331075 10.3390/app16031524 2076-3417 applsci-16-01524_(1).pdf applsci-16-01524_(1).pdf 1 CDE0D4CA1A6C4AD46827060803F68B2E a20bc38df997b2f06b77aff224bf2d4749b66cd8f6d1af6e6511494d099880c3 fe182ef3-074c-11f1-b759-00155d00019a https://dk.um.si/Dokument.php?lang=slv&id=203218 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0