Harnessing AI for enhanced evidence-based laboratory medicine (EBLM)

dc.contributor.authorPillay, Tahir S.
dc.contributor.authorTopcu, Deniz Ilhan
dc.contributor.authorYenice, Sedef
dc.date.accessioned2025-11-21T05:51:16Z
dc.date.available2025-11-21T05:51:16Z
dc.date.issued2025-03
dc.descriptionDATA AVAILABILITY : Data will be made available on request.
dc.description.abstractThe integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies (machine learning, natural language processing and computer vision) advance evidence-based laboratory medicine (EBLM) by automating and optimizing critical processes (formulating clinical questions, conducting literature searches, appraising evidence, and developing clinical guidelines). These reduce the time for systematic reviews, ensuring consistency in appraisal, and enabling real-time updates to guidelines. AI supports personalized medicine by analyzing large datasets, genetic information and electronic health records (EHRs), to tailor diagnostic and treatment plans to patient profiles. Predictive analytics enhance outcomes by leveraging historical data and ongoing monitoring to predict responses and optimize care pathways. Despite the transformative potential, there are challenges. The accuracy, transparency, and explainability of AI algorithms is critical for gaining trust and ensuring ethical deployment. Integration into existing clinical workflows requires collaboration between AI developers and users to ensure seamless user-friendly adoption. Ethical considerations, such as privacy, data security, and algorithmic bias, must also be addressed to mitigate risks and ensure equitable healthcare delivery. Regulatory frameworks, e.g. The EU AI Regulation, emphasize transparency, data governance, and human oversight, particularly for high-risk AI systems. The economic and operational benefits are cost savings, improved diagnostic precision, and enhanced patient outcomes. Future trends (federated learning and self-supervised learning), will enhance the scalability and applicability of AI in EBLM, paving the way for a new era of precision medicine. AI in EBLM has the potential to transform healthcare delivery, improve patient outcomes, and advance personalized/precision medicine.
dc.description.departmentChemical Pathology
dc.description.librarianam2025
dc.description.sdgSDG-03: Good health and well-being
dc.description.sdgSDG-09: Industry, innovation and infrastructure
dc.description.urihttps://www.sciencedirect.com/journal/clinica-chimica-acta
dc.identifier.citationPillay, T.S., Topcu, D.I. & Yenice, S. 2025, 'Harnessing AI for enhanced evidence-based laboratory medicine (EBLM)', Clinica Chimica Acta, vol. 569, art. 120181, pp. 1-9. https://doi.org/10.1016/j.cca.2025.120181.
dc.identifier.issn0009-8981 (print)
dc.identifier.issn1873-3492 (online)
dc.identifier.other10.1016/j.cca.2025.120181
dc.identifier.urihttp://hdl.handle.net/2263/105417
dc.language.isoen
dc.publisherElsevier
dc.rights© 2025 The Author(s). This is an open access article under the CC BY-NC-ND license.
dc.subjectArtificial intelligence (AI)
dc.subjectEvidence-based medicine
dc.subjectEvidence-based laboratory medicine (EBLM)
dc.subjectSystematic review
dc.subjectMeta-analysis
dc.subjectMachine learning
dc.subjectPredictive Analytics
dc.subjectElectronic health records (EHRs)
dc.titleHarnessing AI for enhanced evidence-based laboratory medicine (EBLM)
dc.typeArticle

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