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

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-02-03 DOI:10.1016/j.cca.2025.120181

Tahir S. Pillay , Deniz İlhan Topcu , Sedef Yenice

{"title":"Harnessing AI for enhanced evidence-based laboratory medicine (EBLM)","authors":"Tahir S. Pillay , Deniz İlhan Topcu , Sedef Yenice","doi":"10.1016/j.cca.2025.120181","DOIUrl":null,"url":null,"abstract":"<div><div>The 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, eg. 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.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"569 ","pages":"Article 120181"},"PeriodicalIF":3.2000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinica Chimica Acta","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009898125000609","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

The 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, eg. 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.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.