Volume 7 Number 1 2026
Artificial Intelligence for Quality Control and Error Detection in Diagnostic Laboratories
Saiful Islam 1, Md. Anisur Rahman Anis 2, Md. Abdur Rahim Sarkar 3, Sujibur Rahman 4, Md. Basharuzzaman 5, Faiz un-Nisa 6, Bheesham Kingrani 7, Maryam Zafar 8
Data Modeling 7 (1) 1-16 https://doi.org/10.25163/data.7110945
Submitted: 11 July 2026 Revised: 13 September 2026 Accepted: 23 September 2026 Published: 25 September 2026
Diagnostic error is common, consequential, and, for the most part, difficult to see while it is happening. In the decade since the National Academies of Sciences, Engineering, and Medicine reframed it as a public-health problem, artificial intelligence (AI) has drifted from speculative promise into the working life of diagnostic laboratories, although how far it can be trusted there remains unsettled. This review attempts to take stock. We undertook a structured narrative synthesis, informed by PRISMA-ScR reporting logic, of peer-reviewed literature indexed in PubMed/MEDLINE, Scopus, and Web of Science between January 2019 and July 2026. Twenty-five primary sources were retained, spanning clinical chemistry, microbiology, pathology, cardiology, and adjacent diagnostic fields, and they were synthesised thematically rather than pooled statistically. Reported accuracy or concordance generally fell between 0.78 and 0.99, with the strongest results in narrow, well-defined tasks such as melanoma classification, tuberculosis radiography, and myocardial infarction rule-out. Pathogen-genomics applications reached detection or classification rates of roughly 85% to 97% for organisms including Mycobacterium tuberculosis and Bacillus anthracis. Neurocardiology models achieved AUCs from 0.79 to 0.997, yet performance dropped noticeably once questions became differential or prognostic. Eleven recurring barriers emerged, concentrated around data quality, generalisability, and algorithmic bias, with automation bias and regulatory drift appearing repeatedly as well. Taken together, the evidence suggests that AI can strengthen predictive quality control and error detection, but unevenly. Its safe translation seems to depend less on headline accuracy than on site-specific calibration, explainable outputs, prospective validation, and governance that continues long after deployment.
Keywords: Artificial intelligence; diagnostic error; quality control; clinical laboratory medicine; pathogen genomics; explainable AI
Al-Antari, M. A. (2023). Artificial intelligence for medical diagnostics—Existing and future AI technology. Diagnostics, 13(4), Article 688. https://doi.org/10.3390/diagnostics13040688
Albahra, S., Gorbett, T., Robertson, S., D’Aleo, G., Kumar, S. V. S., Ockunzzi, S., Lallo, D., Hu, B., & Rashidi, H. H. (2023). Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts. Seminars in Diagnostic Pathology, 40(2), 71–87. https://doi.org/10.1053/j.semdp.2023.02.002
Ali, T., & Muhammad, A. (2023). Artificial intelligence for antimicrobial resistance prediction: Challenges and opportunities towards practical implementation. Antibiotics, 12(3), Article 523. https://doi.org/10.3390/antibiotics12030523
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., & Friedman, P. A. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. The Lancet, 394(10201), 861–867. https://doi.org/10.1016/S0140-6736(19)31721-0
Augusto, A. C. C. N. (2025). Evaluating the robustness of AI-based vocal biomarkers against real-world noise: Toward regulatory standards and compliance [Master’s thesis, University of São Paulo].
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
Basem, J., Mani, R., Sun, S., Gilotra, K., Dianati-Maleki, N., & Dashti, R. (2025). Clinical applications of artificial intelligence and machine learning in neurocardiology: A comprehensive review. Frontiers in Cardiovascular Medicine, 12, Article 1525966. https://doi.org/10.3389/fcvm.2025.1525966
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
Browning, L., Jesus, C., Malacrino, S., Guan, Y., White, K., Puddle, A., Alham, N. K., Haghighat, M., Colling, R., Birks, J., & Rittscher, J. (2024). Artificial intelligence-based quality assessment of histopathology whole-slide images within a clinical workflow: Assessment of ‘PathProfiler’ in a diagnostic pathology setting. Diagnostics, 14(10), Article 990. https://doi.org/10.3390/diagnostics14100990
Cau, R., Pisu, F., Porcu, M., Cademartiri, F., Montisci, R., Bassareo, P., & Saba, L. (2023). Machine learning approach in diagnosing Takotsubo cardiomyopathy: The role of the combined evaluation of atrial and ventricular strain, and parametric mapping. International Journal of Cardiology, 373, 124–133. https://doi.org/10.1016/j.ijcard.2022.11.021
Choi, J., Kim, J. Y., Cho, M. S., Kim, M., Kim, J., Oh, I. Y., & Kim, Y. H. (2024). Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms. Heart Rhythm, 21(9), 1647–1655. https://doi.org/10.1016/j.hrthm.2024.03.029
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559–1567. https://doi.org/10.1038/s41591-018-0177-5
Daly, J. E., Delen, D., Han, Z., Smith, R., Honerlaw, J., Cho, K., Bennett, B., & Sippel, J. (2026). AI in clinical decision support systems: Promising applications and strategies for managing data challenges. Journal of Medical Internet Research, 28, Article e71532. https://doi.org/10.2196/71532
Egli, A. (2023). ChatGPT, GPT-4, and other large language models: The next revolution for clinical microbiology? Clinical Infectious Diseases, 77(9), 1322–1328. https://doi.org/10.1093/cid/ciad407
Gador-Whyte, A. P., Sherry, N. L., Brischetto, A., Andersson, P., Bond, K. A., van Hal, S. J., Harris, P. N. A., & Howden, B. P. (2026). Implementation of pathogen genomics in clinical microbiology laboratories. Clinical Microbiology Reviews. Advance online publication.
Gasser, U. (2023). An EU landmark for AI governance. Science, 380(6651), 1203. https://doi.org/10.1126/science.adj2053
Giske, C. G., Bressan, M., Fiechter, F., Hinic, V., Mancini, S., Nolte, O., & Egli, A. (2024). GPT-4-based AI agents—The new expert system for detection of antimicrobial resistance mechanisms? Journal of Clinical Microbiology, 62(10), Article e0068924. https://doi.org/10.1128/jcm.00689-24
Guzman-Garcia, C., Mordonini, M., & Cagnoni, S. (2025). AI-driven applications for diagnostic, clinical decision support and prevention in cardiology, orthopedics, and oncology. In Proceedings of the Ital-IA 2025 Conference on Artificial Intelligence (CEUR Workshop Proceedings, Vol. 4121). CEUR-WS.org. https://ceur-ws.org/Vol-4121/Ital-IA_2025_paper_113.pdf
Hall, K. K., Sheedy, C., Shoemaker-Hunt, S., Wyant, B., Hoffman, L., Bacon, O., Richard, S., Hassol, A., Gall, E., Schneiderman, S., Schoyer, E., Woo, M., Costar, D., LeRoy, L., Gale, B., Fitall, E., Schiff, G., Long, A., Miller, K., & Lim, A. (2020). Making healthcare safer III: A critical analysis of existing and emerging patient safety practices (AHRQ Publication No. 20-0029-EF). Agency for Healthcare Research and Quality.
Hirosawa, T., & Shimizu, T. (2025). A narrative review of artificial intelligence in medical diagnostics. Computers, Materials & Continua, 83(3), 3919–3945. https://doi.org/10.32604/cmc.2025.063803
Laumer, F., Di Vece, D., Cammann, V. L., Würdinger, M., Petkova, V., Schönberger, M., & Templin, C. (2022). Assessment of artificial intelligence in echocardiography diagnostics in differentiating Takotsubo syndrome from myocardial infarction. JAMA Cardiology, 7(5), 494–503. https://doi.org/10.1001/jamacardio.2022.0183
Niu, Z., Kuang, X., Chen, J., Cai, X., & Zhang, P. (2025). The application and challenges of ChatGPT in laboratory medicine. Advances in Laboratory Medicine, 6(4), 385–396. https://doi.org/10.1515/almed-2025-0080
Sallam, M., Snygg, J., Allam, D., Kassem, R., & Damani, M. (2025). Artificial intelligence in clinical medicine: A SWOT analysis of AI progress in diagnostics, therapeutics, and safety. Journal of Innovations in Medical Research, 4(3), 1–20. https://doi.org/10.63593/jimr.2788-7022.2025.06.001
Schnetler, R., van der Vegt, A., Kalke, V. R., Lane, P., & Scott, I. (2025). False hope of a single generalisable AI sepsis prediction model: Bias and proposed mitigation strategies for improving performance based on a retrospective multisite cohort study. BMJ Quality & Safety, 34(9), 580–589. https://doi.org/10.1136/bmjqs-2024-018328
Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M. J., Diamond, J., O’Reilly, P., & Hamilton, P. (2019). Translational AI and deep learning in diagnostic pathology. Frontiers in Medicine, 6, Article 185. https://doi.org/10.3389/fmed.2019.00185
Shimizu, T., Hautz, W. E., van Sassen, C., & Zwaan, L. (2025). The global progress for improving diagnosis: What we’ve learned, what comes next. Diagnosis, 12(4), 529–537. https://doi.org/10.1515/dx-2025-0109
Singh, H., Meyer, A. N. D., & Thomas, E. J. (2014). The frequency of diagnostic errors in outpatient care: Estimations from three large observational studies involving US adult populations. BMJ Quality & Safety, 23(9), 727–731. https://doi.org/10.1136/bmjqs-2013-002627
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 (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Vandenberg, O., Durand, G., Hallin, M., Diefenbach, A., Gant, V., Murray, P., Kozlakidis, Z., & van Belkum, A. (2020). Consolidation of clinical microbiology laboratories and introduction of transformative technologies. Clinical Microbiology Reviews, 33(2), Article e00057-19. https://doi.org/10.1128/CMR.00057-19
Venturi, F., Veronesi, G., Gualandi, A., Magnaterra, E., Scotti, B., Sotiri, I., Baraldi, C., Alessandrini, A. M., Veneziano, L., Vaccari, S., & Dika, E. (2025). From slide to insight: The emerging alliance of digital pathology and AI in melanoma diagnostics. Cancers, 17(22), Article 3696. https://doi.org/10.3390/cancers17223696
Veronesi, G., Curti, N., Gardini, A., Querzoli, G., Castellani, G., & Dika, E. (2025). Machine learning to detect melanoma exploiting nuclei morphology and spatial organization. Scientific Reports, 15, Article 21594. https://doi.org/10.1038/s41598-025-21594-x
Wurcel, V., Cicchetti, A., Garrison, L., Kip, M. M. A., Koffijberg, H., Kolbe, A., Leeflang, M. M. G., Merlin, T., Mestre-Ferrandiz, J., Oortwijn, W., Oosterwijk, C., Tunis, S., & Zamora, B. (2019). The value of diagnostic information in personalised healthcare: A comprehensive concept to facilitate bringing this technology into healthcare systems. Public Health Genomics, 22(1–2), 8–15. https://doi.org/10.1159/000501832
Zhang, X., Liu, C., Sun, Y., You, L., Zhang, X., & Shang, H. (2026). Artificial intelligence medical diagnostic devices: A scoping review of clinical research, challenges and future directions. EngMedicine, 3(1), Article 100120. https://doi.org/10.1016/j.engmed.2026.100120
Artificial Intelligence and Financial Fraud Detection: Survey Evidence on Organizational Integrity and Risk Management from U.S. Professionals