Revolutionizing personalized medicine using artificial intelligence: a meta-analysis of predictive diagnostics and their impacts on drug development

Basit Öğe Kaydı
dc.contributor.authorDaemi, Amin
dc.contributor.authorKalami, Sahar
dc.contributor.authorTahiraga, Ruhiyya Guliyeva
dc.contributor.authorGhanbarpour, Omid
dc.contributor.authorBarghani, Mohammad Reza Rahimi
dc.contributor.authorHooshiar, Mohammad Hosseini
dc.contributor.authorOzbolat, Guluzar
dc.date.accessioned2026-04-25T14:19:47Z
dc.date.available2026-04-25T14:19:47Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractArtificial intelligence (AI) is transforming the landscape of laboratory medicine by enhancing diagnostic accuracy and enabling more personalized care. Given its growing use in clinical settings, evaluating the performance of AI models in diagnostic tasks is essential to inform evidence-based implementation strategies. This meta-analysis systematically assessed the diagnostic effectiveness of AI-based models. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore using predefined keywords related to AI and diagnostic accuracy. From 430 retrieved studies, 17 met the inclusion criteria. Data extracted included study design, AI model type, input modality, and performance metrics such as sensitivity, specificity, and area under the curve (AUC). Random-effects meta-analysis and subgroup analyses were performed to investigate heterogeneity and model-specific trends. The pooled analysis yielded a high combined AUC of 0.9025, indicating strong diagnostic capability of AI models. However, substantial heterogeneity was detected (I2 = 91.01%), attributed to differences in model architecture, diagnostic domains, and data quality. Subgroup analyses showed that convolutional neural networks and random forest models achieved higher AUC values, while domains like endocrinology demonstrated greater performance variability. Funnel plot inspection and sensitivity analysis indicated the presence of publication bias. AI shows strong potential to enhance diagnostic accuracy in personalized laboratory medicine. Nonetheless, methodological heterogeneity and publication bias remain significant challenges. Future research should prioritize standardized evaluation frameworks, transparency, and the development of explainable AI systems to ensure responsible clinical integration.
dc.identifier.doi10.1007/s10238-025-01723-x
dc.identifier.issn1591-8890
dc.identifier.issn1591-9528
dc.identifier.issue1
dc.identifier.pmid40679640
dc.identifier.scopus2-s2.0-105011051061
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s10238-025-01723-x
dc.identifier.urihttps://hdl.handle.net/11486/8189
dc.identifier.volume25
dc.identifier.wosWOS:001532019700002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer-Verlag Italia Srl
dc.relation.ispartofClinical and Experimental Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectArtificial intelligence
dc.subjectDiagnostic accuracy
dc.subjectPersonalized laboratory medicine
dc.subjectSubgroup analysis
dc.subjectExplainable AI
dc.titleRevolutionizing personalized medicine using artificial intelligence: a meta-analysis of predictive diagnostics and their impacts on drug development
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu