Mesterséges intelligencia alkalmazása a diabetológiában: Innovatív lehetőségek

Judit Ildikó Hevesi

doi:10.1556/112.2024.00238

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Scientia et Securitas

Volume/Issue:: Accepted Manuscript / Online First

Mesterséges intelligencia alkalmazása a diabetológiában: Innovatív lehetőségek

Author:

Judit Ildikó Hevesi

Judit Ildikó Hevesi Észak-Pesti Centrumkórház – Honvédkórház, Diabetológia Szakrendelés, Budapest, Magyarország
Nemzeti Közszolgálati Egyetem Doktori Iskola, Hadtudományi kar, Budapest, Magyarország

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hevesi.judit0607@gmail.com https://orcid.org/0000-0002-6491-9273

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Online Publication Date:: 02 Dec 2024

Publication Date:: 02 Dec 2024

Article Category:: Research Article

DOI:: https://doi.org/10.1556/112.2024.00238

Keywords:: Diabetes mellitus; artificial intelligence; Continuous Glucose Monitoring System; insulin pump; data-driven decision making;

Keywords (Hungarian):: cukorbetegség; mesterséges intelligencia; folyamatos szöveti vércukormonitor; inzulinpumpa adatalapú döntéshozatal;

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A cukorbetegség előfordulása világszerte növekszik, és egyre súlyosabb terhet ró az egyén mellett a társadalomra és az egészségügyi rendszerre. A mesterséges intelligencia gyors ütemben terjed az egészségügyben is, és új lehetőségeket kínál a diabétesz diagnosztikában és kezelésben, beleértve a betegek önmenedzselését. Munkám célja, hogy bemutassam a mesterséges intelligencia különféle alkalmazási lehetőségeit a diabetológiában, és konkrét példákkal szemléltessem annak gyakorlati jelentőségét és alkalmazhatóságát. A mesterséges intelligencia alkalmazások folyamatos fejlődése ígéretes támogatást nyújt a cukorbetegség kezelésének holisztikus és betegközpontú megközelítésében, elősegítve annak hatékonyabbá tételét.

Diabetes mellitus (DM) has become one of the fastest-growing global health issues of the 21st century. According to the International Diabetes Federation (IDF), by 2045, the number of patients in the 20–79 age group may reach as high as 783 million globally. The most common form is type 2 diabetes mellitus (T2DM), which accounts for 90% of cases and is characterized by insulin resistance, a reduced effectiveness of the hormone. The treatment of the disease includes lifestyle changes, proper diet, and pharmacological therapy. Type 1 diabetes mellitus (T1DM) is rarer and is an autoimmune condition that causes insulin deficiency, requiring continuous insulin replacement for survival. Diabetes can lead to serious complications such as cardiovascular diseases, blindness, kidney damage, and amputations, which represent significant social and economic burdens. Therefore, early diagnosis of the condition and the timely application of appropriate therapy are crucial. The management of the disease requires lifelong care and self-care, including regular blood glucose monitoring and adjustments to insulin doses based on the individual's circumstances.

The spread of artificial intelligence (AI) in healthcare is bringing significant changes to the management and care of diabetes. In this article, I present various applications of AI in diabetology, illustrating its practical significance and applicability through specific examples.

AI offers opportunities in identifying risk factors for the disease, early diagnosis, the development of personalized therapies, and the detection of complications. Machine learning and predictive models, by analyzing the patient's past and current health data, can help predict diabetes or its complications, assist in prevention, and optimize treatment decisions.AI has also become an essential tool in self-management. The use of Continuous Glucose Monitoring Systems (CGMS) not only enables the continuous monitoring of carbohydrate metabolism but also helps prevent acute complications through predictive alerts related to abnormal low and high blood glucose levels. The cloud-based storage system of CGMS allows healthcare professionals to easily access glucose data and statistical parameters that objectively describe the metabolism. Integration of CGMS with insulin pumps and insulin pens, along with the development of bolus calculators, has improved insulin dose accuracy and adaptation to different life situations.

AI supports healthcare staff in applying a holistic, patient-centered approach, enabling personalized, dynamically adjustable, and safe treatments that can improve therapeutic outcomes and clinical results.

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Scientia et Securitas

Online ISSN:: 2732-2688

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Attila ASZÓDI (Budapest University of Technology and Economics)
Zoltán BIRKNER (University of Pannonia)
Valéria CSÉPE (Research Centre for Natural Sciences, Brain Imaging Centre)
Gergely DELI (University of Public Service)
Tamás DEZSŐ (Migration Research Institute)
Imre DOBÁK (University of Public Service)
Marcell Gyula GÁSPÁR (University of Miskolc)
József HALLER (University of Public Service)
Charaf HASSAN (Budapest University of Technology and Economics)
Zoltán GYŐRI (Hungaricum Committee)
János JÓZSA (Budapest University of Technology and Economics)
András KOLTAY (National Media and Infocommunications Authority)
Gábor KOVÁCS (University of Public Service)
Levente KOVÁCS (Óbuda University)
Melinda KOVÁCS (Hungarian University of Agriculture and Life Sciences (MATE))
Miklós MARÓTH (Avicenna Institue of Middle Eastern Studies )
Judit MÓGOR (Ministry of Interior National Directorate General for Disaster Management)
József PALLO (University of Public Service)
István SABJANICS (Ministry of Interior)
Péter SZABÓ (Hungarian University of Agriculture and Life Sciences (MATE))
Miklós SZÓCSKA (Semmelweis University)

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Scientia et Securitas
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