Data Modeling
Mathematical and Computational Data Modeling | Online ISSN 3143-9217
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Volume 7 Number 1 2026
REVIEWS (Open Access)
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
Abstract
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
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