Artificial Intelligence in Clinical Decision Support:Current Applications, Challenges, and Future Directions
;
artificial intelligence, clinical decision support system, clinical evidence, data shift, medical AI governance초록
Background: Artificial intelligence (AI) has become an important area of clinical decision support research, yet high performance in retrospective datasets does not establish patient benefit. Objective: To critically examine the applications, limitations, clinical evidence, and governance of AI-enabled clinical decision support systems (AI-CDSS). Approach: This focused narrative review synthesizes selected peer-reviewed studies, reporting standards, and regulatory guidance; it is not a systematic review. Findings: AI-CDSS can support imaging interpretation, risk stratification, clinical documentation, and monitoring. Prospective evidence suggests that improvements in care processes do not consistently translate into improvements in patient-important outcomes. Data shift, calibration, bias, automation effects, and workflow design remain major barriers. Conclusion: Deployment should be guided by intended-use-specific validation, prospective evaluation, human accountability, and ongoing surveillance. The primary benchmark should be demonstrable clinical value rather than algorithmic accuracy alone.
##submission.citations##
Agweyu, A., Mwaniki, P., Menon, V., Korom, R., Isaaka, L., Wanyama, C., Gill, J., Kiptinness, S., Adan, N., Emmanuel-Fabula, M., Riley, R. D., Archer, L., Denniston, A. K., Liu, X., & Mateen, B. A. (2026). Generative AI-enabled clinical decision support system in primary care: A pragmatic, cluster-randomized trial. Nature Medicine, 32, 3032–3039. https://doi.org/10.1038/s41591-026-04503-6
Biffi, A., Castellini, G., Del Castillo, G., De Nard, F., Vismara, C., Cabitza, F., Corrao, G., & Gianola, S. (2026). Effectiveness of computerized decision support systems linked to electronic health records: An updated systematic review with meta-analysis. International Journal of Medical Informatics, 207, Article 106220. https://doi.org/10.1016/j.ijmedinf.2025.106220
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., & SPIRIT-AI and CONSORT-AI Working Group. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nature Medicine, 26, 1351–1363. https://doi.org/10.1038/s41591-020-1037-7
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., & SPIRIT-AI and CONSORT-AI Working Group. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26, 1364–1374. https://doi.org/10.1038/s41591-020-1034-x
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Scharp, D., Meyers, K., Nadkarni, G., Federman, A., Sarkisian, C., Levin, M. A., Perry, S., Gownivaripally, P., Egorova, N. N., Ouyang, Y., Vacas, S., & Hofer, I. (2026). Artificial intelligence-driven clinical decision support for postoperative delirium prevention: A pragmatic randomized controlled trial. Anesthesia & Analgesia. Advance online publication. https://doi.org/10.1213/ANE.0000000000008318
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44–56. https://doi.org/10.1038/s41591-018-0300-7
U.S. Food and Drug Administration. (2026, January 29). Clinical decision support software: Guidance for industry and Food and Drug Administration staff. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software
World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. https://www.who.int/publications/i/item/9789240029200
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2, 719–731. https://doi.org/10.1038/s41551-018-0305-z
