Artificial Intelligence in Medical Laboratory Diagnostics: Current Applications, Challenges and Future Directions

Author

Ram Pachauri


Abstract

 Artificial intelligence (AI), including machine learning and deep learning, is increasingly being incorporated into medical laboratory diagnostics. Clinical laboratories generate large volumes of structured numerical, categorical, image-based, molecular and longitudinal data, creating an environment in which AI can support pattern recognition, classification, prediction, workflow optimization and decision support. This narrative review summarizes current applications of AI across the laboratory diagnostic pathway, with emphasis on pre-analytical processes, clinical chemistry, hematology, microbiology, molecular diagnostics, transfusion medicine and digital pathology. AI can assist with specimen quality assessment, result validation, cell and microorganism classification, image interpretation, antimicrobial susceptibility prediction, molecular data analysis and identification of clinically relevant patterns across multimodal datasets. However, high reported model performance does not automatically establish clinical usefulness. Major barriers include limited external and multicentre validation, dataset shift, class imbalance, hidden bias, poor interoperability, cybersecurity and privacy concerns, explainability limitations, workflow disruption, regulatory uncertainty, costs and the need for continuing human oversight. Recent reporting and governance frameworks, including TRIPOD+AI, STARD-AI and FUTURE-AI, emphasize transparent reporting, rigorous evaluation, fairness, traceability, usability, robustness and explainability. The future of laboratory AI is therefore likely to depend less on isolated high-performing algorithms and more on validated, interoperable and clinically integrated systems that augment laboratory professionals rather than replace them. Multidisciplinary collaboration among laboratory scientists, clinicians, pathologists, data scientists, engineers, regulators and patients will be essential for safe and equitable implementation.


Keywords

artificial intelligence; machine learning; deep learning; medical laboratory digital pathology; biomarkers;



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References


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