Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases
The aim of this study is to explore the application of artificial intelligence (AI) in diagnosing endocrine disorders, with a specific focus on diabetes and thyroid diseases. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, has emerged as a pivotal techn...
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| Format: | Article |
| Language: | English |
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Specijalna bolnica za bolesti štitaste žlezde i bolesti metabolizma Zlatibor
2024-01-01
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| Series: | Medicinski Glasnik Specijalne Bolnice za Bolesti Štitaste Žlezde i Bolesti Metabolizma "Zlatibor" |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1821-1925/2024/1821-19252495039K.pdf |
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| _version_ | 1841554199013228544 |
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| author | Kimi Milić Marko Sinanović Šćepan Prodović Tanja Ilanković Tanja |
| author_facet | Kimi Milić Marko Sinanović Šćepan Prodović Tanja Ilanković Tanja |
| author_sort | Kimi Milić Marko |
| collection | DOAJ |
| description | The aim of this study is to explore the application of artificial intelligence (AI) in diagnosing endocrine disorders, with a specific focus on diabetes and thyroid diseases. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, has emerged as a pivotal technology in medicine, enabling early diagnosis and precise evaluation of complex medical conditions. This paper provides an overview of current technological solutions, including an analysis of the accuracy, sensitivity, and specificity of various AI algorithms, as well as their efficiency compared to traditional diagnostic methods. Methodologically, the study relies on a systematic review of the existing literature and case studies analyzing the use of algorithms such as convolutional neural networks (CNN) and support vector machines (SVM). The results show that AI tools provide a significant advantage over classical approaches, with accuracy exceeding 90% in identifying key biomarkers and abnormalities in laboratory test results. The role of algorithms in personalizing diagnostic protocols and optimizing treatment workflows is particularly highlighted. The conclusion emphasizes the potential of artificial intelligence to advance the diagnosis of endocrine disorders and contribute to the development of sustainable, high-precision solutions in the healthcare system. At the same time, challenges such as ethical concerns, integration into clinical practice, and the need for data standardization are discussed. Future research should focus on optimizing algorithms and implementing them in real-world clinical settings. |
| format | Article |
| id | doaj-art-a0f8b3f9667e4cf4a6bb3e616e3138cd |
| institution | Kabale University |
| issn | 1821-1925 2406-131X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Specijalna bolnica za bolesti štitaste žlezde i bolesti metabolizma Zlatibor |
| record_format | Article |
| series | Medicinski Glasnik Specijalne Bolnice za Bolesti Štitaste Žlezde i Bolesti Metabolizma "Zlatibor" |
| spelling | doaj-art-a0f8b3f9667e4cf4a6bb3e616e3138cd2025-01-08T16:29:19ZengSpecijalna bolnica za bolesti štitaste žlezde i bolesti metabolizma ZlatiborMedicinski Glasnik Specijalne Bolnice za Bolesti Štitaste Žlezde i Bolesti Metabolizma "Zlatibor"1821-19252406-131X2024-01-012995396310.5937/mgiszm2495039K1821-19252495039KArtificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseasesKimi Milić Marko0Sinanović Šćepan1Prodović Tanja2Ilanković Tanja3Visoka medicinska škola strukovnih studija "Milutin Milanković", Beograd, SerbiaVisoka medicinska škola strukovnih studija "Milutin Milanković", Beograd, SerbiaVisoka medicinska škola strukovnih studija "Milutin Milanković", Beograd, SerbiaVisoka medicinska škola strukovnih studija "Milutin Milanković", Beograd, SerbiaThe aim of this study is to explore the application of artificial intelligence (AI) in diagnosing endocrine disorders, with a specific focus on diabetes and thyroid diseases. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, has emerged as a pivotal technology in medicine, enabling early diagnosis and precise evaluation of complex medical conditions. This paper provides an overview of current technological solutions, including an analysis of the accuracy, sensitivity, and specificity of various AI algorithms, as well as their efficiency compared to traditional diagnostic methods. Methodologically, the study relies on a systematic review of the existing literature and case studies analyzing the use of algorithms such as convolutional neural networks (CNN) and support vector machines (SVM). The results show that AI tools provide a significant advantage over classical approaches, with accuracy exceeding 90% in identifying key biomarkers and abnormalities in laboratory test results. The role of algorithms in personalizing diagnostic protocols and optimizing treatment workflows is particularly highlighted. The conclusion emphasizes the potential of artificial intelligence to advance the diagnosis of endocrine disorders and contribute to the development of sustainable, high-precision solutions in the healthcare system. At the same time, challenges such as ethical concerns, integration into clinical practice, and the need for data standardization are discussed. Future research should focus on optimizing algorithms and implementing them in real-world clinical settings.https://scindeks-clanci.ceon.rs/data/pdf/1821-1925/2024/1821-19252495039K.pdfartificial intelligenceendocrine disordersdiabetesthyroid diseasesdiagnosticsmachine learning algorithms |
| spellingShingle | Kimi Milić Marko Sinanović Šćepan Prodović Tanja Ilanković Tanja Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases Medicinski Glasnik Specijalne Bolnice za Bolesti Štitaste Žlezde i Bolesti Metabolizma "Zlatibor" artificial intelligence endocrine disorders diabetes thyroid diseases diagnostics machine learning algorithms |
| title | Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases |
| title_full | Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases |
| title_fullStr | Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases |
| title_full_unstemmed | Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases |
| title_short | Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases |
| title_sort | artificial intelligence in the diagnosis of endocrine disorders a focus on diabetes and thyroid diseases |
| topic | artificial intelligence endocrine disorders diabetes thyroid diseases diagnostics machine learning algorithms |
| url | https://scindeks-clanci.ceon.rs/data/pdf/1821-1925/2024/1821-19252495039K.pdf |
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