Bridging expertise with machine learning and automated machine learning in clinical medicine

In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine healthcare by making artificial intelligence (AI) tec...

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Bibliographic Details
Published inAnnals of the Academy of Medicine, Singapore Vol. 53; no. 3 - Correct DOI; pp. 129 - 131
Main Authors Lee, Chien-Chang, Park, James Yeongjun, Hsu, Wan-Ting
Format Journal Article
LanguageEnglish
Published 27.03.2024
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Summary:In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine healthcare by making artificial intelligence (AI) technologies accessible to those without advanced computational skills.1 This enables the development of effective AI models that could rival or exceed the accuracy of traditional machine learning (ML) approaches and human diagnostic methods.
ISSN:0304-4602
0304-4602
DOI:10.47102/annals-acadmedsg.202481