Artificial Intelligence and Algorithmic Decision Support for Patient Safety in Türkiye: A Scoping Review and Evidence Gap Map

Öz

Artificial intelligence (AI), machine learning, predictive analytics, and algorithmic decision-support systems are increasingly used in healthcare to support risk prediction, patient monitoring, clinical decision-making, and safety-related processes. However, their explicit integration into patient safety-related healthcare research in Türkiye remains unclear. This scoping review and evidence gap mapping study aimed to identify Türkiye-related studies addressing AI and algorithmic decision-support applications in patient safety-related healthcare domains. Searches were conducted in March 2026 in TR Dizin and Web of Science Core Collection without publication year restriction. TR Dizin records were first screened using Turkish patient safety-related keywords and then assessed for predefined AI-related terminology, while Web of Science searches combined AI, machine learning, predictive analytics, algorithmic decision support, and patient safety terms. The review followed PRISMA-ScR principles and predefined eligibility criteria. A total of 614 records were identified, including 486 from TR Dizin and 128 from Web of Science. After screening and full-text assessment, five studies met the inclusion criteria. No TR Dizin-indexed study explicitly combined AI-related terminology with patient safety concepts using the predefined search strategy. The included studies addressed patient safety relevance mainly through infection surveillance, procedural safety, critical care risk prediction, ICU triage, and diagnostic support. Identified safety mechanisms included early risk detection, diagnostic error reduction, infection prevention, procedural standardization, automated monitoring, and clinical decision support. The findings indicate that AI-oriented patient safety research related to Türkiye remains at an early stage. Future research should prioritize validated AI-based decision-support tools, predictive risk models, and safety-monitoring systems integrated into digital healthcare infrastructures.

Anahtar Kelimeler

• Artificial intelligence
• Machine learning
• Algorithmic decision support
• Predictive analytics
• Patient safety
• Digital healthcare

Kaynakça

1. Baddal, B., Taner, F., & Uzun Ozsahin, D. (2024). Harnessing of artificial intelligence for the diagnosis and prevention of hospital-acquired infections: A systematic review. Diagnostics, 14(5), Article 484. https://doi.org/10.3390/diagnostics14050484
2. Bates, D. W., Cohen, M., Leape, L. L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the frequency of errors in medicine using information technology. Journal of the American Medical Informatics Association, 8(4), 299–308. https://doi.org/10.1136/jamia.2001.0080299
3. Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2), Article 125. https://doi.org/10.3390/healthcare12020125
4. Cascella, M., Semeraro, F., Montomoli, J., Bellini, V., Piazza & Elena O. (2024). The breakthrough of large language models release for medical applications: 1-year timeline and perspectives. Journal of Medical Systems, 48(1), Article 22. https://doi.org/10.1007/s10916-024-02045-3
5. De Micco, F., Di Palma, G., Ferorelli, D., De Benedictis, A., Tomassini, L., Tambone, V., Cingolani, M., & Scendoni, R. (2025). Artificial intelligence in healthcare: Transforming patient safety with intelligent systems: A systematic review. Frontiers in Medicine, 11, Article 1522554. https://doi.org/10.3389/fmed.2024.1522554
6. El Arab, R. A., Almoosa, Z., Alkhunaizi, M., Abuadas, F. H., & Somerville, J. (2025). Artificial intelligence in hospital infection prevention: An integrative review. Frontiers in Public Health, 13, Article 1547450. https://doi.org/10.3389/fpubh.2025.1547450
7. Erginoğlu, S. E., Ülgen, N. K., Yiğit, N., Nazlıgül, A. S., & Akkurt, M. O. (2026). Multimodal large language model for fracture detection in emergency orthopedic trauma: A diagnostic accuracy study. Diagnostics, 16(3), Article 476. https://doi.org/10.3390/diagnostics16030476
8. Esmaeilzadeh, P. (2024). Challenges and strategies for wide-scale artificial intelligence deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine, 151, Article 102861. https://doi.org/10.1016/j.artmed.2024.102861

9. Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
10. He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2025). Foundation model for advancing healthcare: Challenges, opportunities and future directions. IEEE Reviews in Biomedical Engineering, 18, 172–191. https://doi.org/10.1109/RBME.2024.3496744
11. Labkoff, S., Oladimeji B., Kannry J., Solomonides A., Leftwich R., Koski E., Joseph A.L., Lopez-Gonzalez M., Fleisher L.A., Nolen K., Dutta S., Levy D.R., Price A., Barr P.J., Hron J.D., Lin B., Srivastava G., Pastor N., Luque U.S., Bui T.T.T., Singh R., Williams T., Weiner M.G., Naumann T., Sittig D.F., Jackson G.P., Quintana Y. (2024). Toward a responsible future: Recommendations for AI-enabled clinical decision support. Journal of the American Medical Informatics Association, 31(11), 2730–2739. https://doi.org/10.1093/jamia/ocae209
12. Makary, M. A., & Daniel, M. (2016). Medical error: The third leading cause of death in the US. BMJ, 353, Article i2139. https://doi.org/10.1136/bmj.i2139
13. Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), Article e26297. https://doi.org/10.1016/j.heliyon.2024.e26297
14. Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., Denniston, A. K., Ghassemi, M., Heinze, G., Kengne, A. P., Maier-Hein, L., Liu, X., Logullo, P., McCradden, M. D., Liu, N., … van Smeden, M. (2025). PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ, 388, Article e082505. https://doi.org/10.1136/bmj-2024-082505
15. Muluk, S. Y. (2024). Enhancing musculoskeletal injection safety: Evaluating checklists generated by artificial intelligence and revising the preformed checklist. Cureus, 16(5), Article e59708. https://doi.org/10.7759/cureus.59708
16. Nong, P., Adler-Milstein, J., Apathy, N. C., Holmgren, A. J., & Everson, J. (2025). Current use and evaluation of artificial intelligence and predictive models in US hospitals. Health Affairs, 44(1), 90–98. https://doi.org/10.1377/hlthaff.2024.00842
17. Pantanowitz, L., Pearce, T., Abukhiran, I., Hanna, M., Wheeler, S., Soong, T. R., Tafti, A. P., Pantanowitz, J., Lu, M. Y., Mahmood, F., Gu, Q., & Rashidi, H. H. (2025). Nongenerative artificial intelligence in medicine: Advancements and applications in supervised and unsupervised machine learning. Modern Pathology, 38(3), Article 100680. https://doi.org/10.1016/j.modpat.2024.100680
18. Rahmouni, H. B., Ben El Hadj Hassine, N., Chouchen, M., Ceylan, H. İ., Muntean, R. I., Bragazzi, N. L., & Dergaa, I. (2025). Healthcare 5.0-driven clinical intelligence: The learn–predict–monitor–detect–correct framework for systematic artificial intelligence integration in critical care. Healthcare, 13(20), Article 2553. https://doi.org/10.3390/healthcare13202553
19. Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., & Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 358, Article j4008. https://doi.org/10.1136/bmj.j4008
20. Silcox, C., Zimlichmann, E., Huber, K., Rowen, N., Saunders, R., McClellan, M., Kahn, C. N., Salzberg, C. A., & Bates, D. W. (2024). The potential for artificial intelligence to transform healthcare: Perspectives from international health leaders. npj Digital Medicine, 7(1), Article 88. https://doi.org/10.1038/s41746-024-01097-6
21. Sun, H., Kang, M., Zhang, H., Jia, J., & Wang, Q. (2025). Machine learning for predicting mortality in intensive care unit patients: A prognostic performance systematic review and meta-analysis. Nursing in Critical Care, 30(6), Article e70206. https://doi.org/10.1111/nicc.70206
22. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
23. Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews: Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
24. Varnosfaderani, S. M., & Forouzanfar, M. (2024). The role of AI in hospitals and clinics: Transforming healthcare in the 21st century. Bioengineering, 11(4), Article 337. https://doi.org/10.3390/bioengineering11040337
25. Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., & Bossuyt, P. M. M. (2011). QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8), 529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
26. Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51–58. https://doi.org/10.7326/M18-1376
27. World Health Organization. (2019). Patient safety: Global action on patient safety. World Health Organization. https://iris.who.int/items/391bf876-923c-4f51-8f06-0635c0a5aeb1
28. Yilmaz Başer, H., Evran, T., & Cifci, M. A. (2025). Machine learning-augmented triage for sepsis: Real-time ICU mortality prediction using SHAP-explained meta-ensemble models. Biomedicines, 13(6), Article 1449. https://doi.org/10.3390/biomedicines13061449
29. Zhou, B., Yang, G., Shi, Z., & Ma, S. (2024). Natural language processing for smart healthcare. IEEE Reviews in Biomedical Engineering, 17, 4–18. https://doi.org/10.1109/RBME.2022.3210270