From accuracy to clinical utility: diagnostic effectiveness of tests, artificial intelligence and decision support in evidence-based medicine
DOI:
https://doi.org/10.71068/hs000054Keywords:
diagnosis, diagnostic accuracy, evidence-based medicine, artificial intelligence, clinical utility.Abstract
Introduction: Evaluation of diagnostic technologies requires moving beyond isolated interpretation of sensitivity and specificity to consider heterogeneity, external validation, applicability, and clinical utility. Recent evidence demonstrates substantial methodological variation among diagnostic-accuracy reviews, within medical imaging. Meanwhile, artificial intelligence has achieved promising results in tasks such as fracture detection, although its performance requires comparison with clinicians and evaluation under real clinical conditions. Objective: To systematically analyze recent evidence on diagnostic effectiveness by comparing conventional tests, biomarkers, artificial intelligence, and point-of-care technologies from diagnostic accuracy to clinical utility. Methodology: A systematic review was conducted through structured searching, study selection, critical appraisal, and standardized data extraction. Diagnostic accuracy, reference standards, validation, heterogeneity, and clinical applicability were examined. Results: Automated systems demonstrated relevant diagnostic capabilities across different clinical scenarios; however, performance varied according to population characteristics, disease prevalence, data quality, and external validation. Artificial intelligence demonstrated favorable results for automated diabetic retinopathy screening under real-world conditions. Emergency medicine point-of-care ultrasound also showed clinical usefulness for diagnosing acute cholecystitis, whereas blood biomarkers demonstrated potential diagnostic value for ischemic stroke assessment. Conclusions: High diagnostic accuracy alone does not guarantee clinical effectiveness. Implementation requires external validation, appropriate reference standards, bias assessment, contextual evaluation, and evidence that diagnostic technologies improve decision-making, timeliness of care, diagnostic pathways, or outcomes that are meaningful to patients. These requirements are essential for safe translation into routine care.
References
1. White SJ, Phua QS, Lu L, Yaxley KL, McInnes MDF, To M. Heterogeneity in systematic reviews of medical imaging diagnostic test accuracy studies: a systematic review. JAMA Netw Open. 2024;7(2):e240649. https://doi.org/10.1001/jamanetworkopen.2024.0649.
2. Bossuyt PM, et al. Contemporary methodological challenges in diagnostic test accuracy research and evidence synthesis. J Clin Epidemiol. 2022;149:1-10. https://doi.org/10.1016/j.jclinepi.2022.05.015.
3. Nowroozi A, Salehi MA, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra MK. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clin Radiol. 2024;79(8):579-588. https://doi.org/10.1016/j.crad.2024.04.009.
4. Nagendran M, et al. Clinical performance of artificial intelligence versus clinicians in diagnostic tasks: updated systematic review. Lancet Digit Health. 2022;4:e715-e726. https://doi.org/10.1016/S2589-7500(22)00145-2.
5. McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med. 2024;7:114. https://doi.org/10.1038/s41746-024-01106-8.
6. Cohen JF, et al. Risk of bias and applicability in diagnostic accuracy studies of artificial intelligence: systematic review. Radiology. 2023;307:e221712. https://doi.org/10.1148/radiol.221712.
7. Joseph S, Selvaraj J, Mani I, Kumaragurupari T, Shang X, Mudgil P, et al. Diagnostic accuracy of artificial intelligence-based automated diabetic retinopathy screening in real-world settings: a systematic review and meta-analysis. Am J Ophthalmol. 2024;263:214-230. https://doi.org/10.1016/j.ajo.2024.02.012.
8. Liu X, et al. Reporting and validation of artificial intelligence diagnostic models in clinical medicine: systematic review. NPJ Digit Med. 2023;6:120. https://doi.org/10.1038/s41746-023-00866-7.
9. Wilson SJ, Thavanathan R, Cheng W, Stuart J, Kim DJ, Glen P, et al. Test characteristics of emergency medicine-performed point-of-care ultrasound for the diagnosis of acute cholecystitis: a systematic review and meta-analysis. Ann Emerg Med. 2024;83(3):235-246. https://doi.org/10.1016/j.annemergmed.2023.09.005.
10. Popat A, Harikrishnan S, Seby N, Sen U, Patel SK, Mittal L, et al. Utilization of point-of-care ultrasound as an imaging modality in the emergency department: a systematic review and meta-analysis. Cureus. 2024;16(1):e52371. https://doi.org/10.7759/cureus.52371.
11. Kawada T, Shim SR, Quhal F, Rajwa P, Pradere B, Yanagisawa T, et al. Diagnostic accuracy of liquid biomarkers for clinically significant prostate cancer detection: a systematic review and diagnostic meta-analysis of multiple thresholds. Eur Urol Oncol. 2024;7(4):649-662. https://doi.org/10.1016/j.euo.2023.10.029.
12. Ruksakulpiwat S, Zhou W, Phianhasin L, Benjasirisan C, Su T, Aldossary HM, et al. A systematic review and meta-analysis assessing the accuracy of blood biomarkers for the diagnosis of ischemic stroke in adult and elderly populations. eNeuro. 2024;11(11):ENEURO. 0302-24.2024. https://doi.org/10.1523/ENEURO.0302-24.2024.
13. Jeong JH, Shim SR, Han S, Hwang I, Ihm C. Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis. Front Microbiol. 2024;15:1506127. https://doi.org/10.3389/fmicb.2024.1506127.
14. Webster KE, Parkhouse T, Dawson S, Jones HE, Brown EL, Hay AD, et al. Diagnostic accuracy of point-of-care tests for acute respiratory infection: a systematic review of reviews. Health Technol Assess. 2024. https://doi.org/10.3310/JLCP4570
15. McLellan J, Heneghan C, Roberts N, Plüddemann A. Accuracy of self-diagnosis in conditions commonly managed in primary care: diagnostic accuracy systematic review and meta-analysis. BMJ Open. 2023;13:e065748. https://doi.org/10.1136/bmjopen-2022-065748.
16. Dagens AB, Rojek A, Sigfrid L, Plüddemann A. The diagnostic accuracy of rapid diagnostic tests for Ebola virus disease: a systematic review. Clin Microbiol Infect. 2023;29(2):171-181. https://doi.org/10.1016/j.cmi.2022.09.014.
17. Nadour N, Duguet T, Zahedi S, Figoni H, Liard R. Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis. BMC Prim Care. 2025;26:384. https://doi.org/10.1186/s12875-025-03073-9.
18. Lawrence R, Dodsworth E, Massou E, Sherlaw-Johnson C, et al. Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review. EClinicalMedicine. 2025;83:103228. https://doi.org/10.1016/j.eclinm.2025.103228.
19. Bunnell A, Valdez D, Strand F, Glaser Y, Sadowski P, Shepherd JA. Artificial intelligence-enhanced handheld breast ultrasound for screening: a systematic review of diagnostic test accuracy. PLoS Digit Health. 2025;4(9):e0001019. https://doi.org/10.1371/journal.pdig.0001019.
20. Wadie P, Zakher B, Elgazzar K, Alsbakhi A, Alhejaily AMG. AI in point-of-care imaging for clinical decision support: systematic review of diagnostic accuracy, task-shifting, and explainability. JMIR AI. 2026;5:e80928. https://doi.org/10.2196/80928.
21. Eskandar K. Artificial intelligence in CT imaging: a systematic review of diagnostic accuracy, clinical decision-support impact, and integration pathways. iRADIOLOGY. 2025;3(6):434-445. https://doi.org/10.1002/ird3.70046.
22. Vasey B, et al. Evaluation frameworks for clinical artificial intelligence and diagnostic decision support: systematic review. Nat Med. 2022;28:924-933. https://doi.org/10.1038/s41591-022-01783-2.
23. Suprianto D, Fitri LE, Sabarudin A, et al. A systematic review and meta-analysis of diagnostic test accuracy for human toxoplasmosis: performance, populations, and validation gaps. Med Devices Evid Res. 2026;19:569457. https://doi.org/10.2147/MDER.S569457.
24. Freeman K, et al. Diagnostic test accuracy evidence and downstream patient outcomes: systematic review of comparative pathways. BMJ Evid Based Med. 2023;28:289-297. https://doi.org/10.1136/bmjebm-2022-112032.
25. Kellerhuis BE, Jenniskens K, Kusters MPT, Schuit E, Hooft L, Moons KGM, et al. Expert panel as reference standard procedure in diagnostic accuracy studies: a systematic scoping review and methodological guidance. Diagn Progn Res. 2025;9:12. https://doi.org/10.1186/s41512-025-00195-7.
26. Nuutinen M, Leskelä RL. Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support systems. Health Technol. 2023. https://doi.org/10.1007/s12553-023-00763-1
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Jhon Fischer Gallardo Zapata
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in this journal are available under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. This licence authorises third parties to download, copy, distribute, adapt and reuse the contents, including for commercial purposes, provided that credit is given to the original author of the work.
