| Title: | Symbiosis in health : the powerful alliance of AI and propensity score matching in real world medical data analysis |
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| Authors: | ID Kokol, Peter (Author)
ID Žlahtič, Bojan (Author)
ID Blažun Vošner, Helena (Author)
ID Završnik, Jernej (Author)
ID Završnik, Tadej (Author) |
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| Files: |  applsci-16-01524_(1).pdf (2,61 MB)
MD5: CDE0D4CA1A6C4AD46827060803F68B2E
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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. |
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| Keywords: | propensity score matching, artificial intelligence, real world decision making, synthetic thematic analysis, research landscapes |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 01.12.2025 |
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| Article acceptance date: | 30.01.2026 |
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| Publication date: | 03.02.2026 |
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| Publisher: | MDPI |
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| Year of publishing: | 2026 |
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| Number of pages: | 21 str. |
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| Numbering: | Vol. 16, iss. 3, [article no.] 1524 |
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| PID: | 20.500.12556/DKUM-97008  |
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| UDC: | 004.8 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 267331075 |
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| DOI: | 10.3390/app16031524 |
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| Copyright: | © 2026 by the authors
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| Publication date in DKUM: | 11.02.2026 |
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| Views: | 125 |
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| Downloads: | 4 |
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| Metadata: |    |
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| Categories: | Misc.
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