Artificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Review

AuthorAlireza Keshtkaren
AuthorNazanin Ayarehen
AuthorFarnaz Atighien
AuthorReza Hamidien
AuthorParsa Yazdanpanahien
AuthorAlireza Karimien
AuthorArzhang Naserien
AuthorFatemeh Hosseinien
AuthorMohammad Hossein Dabbaghmaneshen
OrcidNazanin Ayareh [0000-0002-0027-4322]en
OrcidFarnaz Atighi [0009-0005-2353-7139]en
OrcidParsa Yazdanpanahi [0009-0004-3969-2830]en
OrcidAlireza Karimi [0009-0008-5154-5074]en
OrcidArzhang Naseri [0000-0002-4095-2351]en
OrcidMohammad Hossein Dabbaghmanesh [0000-0002-4877-0376]en
Issued Date2024-07-31en
AbstractContext: The diagnostic methods for diabetes mellitus (DM), a chronic metabolic disorder characterized by elevated blood sugar levels, are rapidly evolving thanks to artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL). This review explores the applications of AI in risk assessment and diagnosing different types of diabetes. Evidence Acquisition: The review highlights the effectiveness of various ML models, including support vector machines (SVMs), random forests (RFs), and DL techniques like convolutional neural networks (CNNs), in achieving high diagnostic accuracy. Challenges include limited data availability, interpretability of complex models, and the need for standardized performance metrics. Results: Machine learning methods like SVMs and RFs are highly effective at diagnosing different types of diabetes, and DL techniques like CNNs also show great promise. Conclusions: Overall, AI has immense potential to revolutionize diabetes diagnosis by facilitating risk assessment and early detection, improving treatment efficacy, and preventing severe complications.en
DOIhttps://doi.org/10.5812/semj-146903en
KeywordDiabetes Mellitusen
KeywordDiagnosisen
KeywordMachine Learning (ML)en
KeywordDeep Learning (DL)en
KeywordArtificial Intelligence (AI)en
PublisherBrieflandsen
TitleArtificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Reviewen
TypeReview Articleen

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