Applicability of Artificial Intelligence in the Field of Clinical Lipidology: A Narrative Review
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The development of advanced technologies in artificial intelligence (AI) has expanded its applications across various fields. Machine learning (ML), a subcategory of AI, enables computers to recognize patterns within extensive datasets. Furthermore, deep learning, a specialized form of ML, processes inputs through neural network architectures inspired by biological processes. The field of clinical lipidology has experienced significant growth over the past few years, and recently, it has begun to intersect with AI. Consequently, the purpose of this narrative review is to examine the applications of AI in clinical lipidology. This review evaluates various publications concerning the diagnosis of familial hypercholesterolemia, estimation of low-density lipoprotein cholesterol (LDL-C) levels, prediction of lipid goal attainment, challenges associated with statin use, and the influence of cardiometabolic and dietary factors on the discordance between apolipoprotein B and LDL-C. Given the concerns surrounding AI techniques, such as ethical dilemmas, opacity, limited reproducibility, and methodological constraints, it is prudent to establish a framework that enables the medical community to accurately interpret and utilize these emerging technological tools.
Abstract
The development of advanced technologies in artificial intelligence (AI) has expanded its applications across various fields. Machine learning (ML), a subcategory of AI, enables computers to recognize patterns within extensive datasets. Furthermore, deep learning, a specialized form of ML, processes inputs through neural network architectures inspired by biological processes. The field of clinical lipidology has experienced significant growth over the past few years, and recently, it has begun to intersect with AI. Consequently, the purpose of this narrative review is to examine the applications of AI in clinical lipidology. This review evaluates various publications concerning the diagnosis of familial hypercholesterolemia, estimation of low-density lipoprotein cholesterol (LDL-C) levels, prediction of lipid goal attainment, challenges associated with statin use, and the influence of cardiometabolic and dietary factors on the discordance between apolipoprotein B and LDL-C. Given the concerns surrounding AI techniques, such as ethical dilemmas, opacity, limited reproducibility, and methodological constraints, it is prudent to establish a framework that enables the medical community to accurately interpret and utilize these emerging technological tools.
참고문헌 (37)
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