Opis

• Tytuł:
  The use of artificial intelligence in the diagnosis and detection of complications of diabetes
• Twórca:
  Ziajor, Seweryn;
  Tomasik, Justyna;
  Sajdak, Piotr;
  Turski, Mikołaj;
  Bednarski, Artur;
  Stodolak, Marcel;
  Szydłowski, Łukasz;
  Żurowska, Klaudia;
  Krużel, Aleksandra;
  Kłos, Kamil;
  Dębik, Marika
• Opis:
  Introduction: Diabetes poses a significant global health challenge, impacting patient well-being and longevity. Despite advances in diagnosis and treatment, the prevalence of diabetes continues to rise, with projections indicating a substantial increase in affected individuals in the coming years. The complications of diabetes, including cardiovascular disease, retinopathy, nephropathy, and neuropathy, underscore the importance of early detection and management. In this context, artificial intelligence (AI) offers promising opportunities to revolutionize diabetes care, enabling faster diagnostics, more effective treatment strategies. Description of the State of Knowledge: Artificial intelligence (AI) has emerged as a transformative force in healthcare, leveraging machine learning and deep learning algorithms to analyze vast amounts of medical data. These algorithms enable more accurate diagnosis, prediction of disease onset, and early detection of complications associated with diabetes. Machine learning models, including support vector machines and neural networks, have shown promise in identifying diabetes risk factors and predicting disease progression. Deep learning techniques, with their ability to analyze complex data patterns, offer further insights into diabetes diagnosis. Additionally, fuzzy cognitive maps provide a framework for decision-making based on patient data, enhancing early detection efforts. Summary: Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes. By harnessing the power of AI algorithms, healthcare providers can enhance diagnostic accuracy, predict disease progression, and implement targeted interventions.
• Dostępność obiektu:
  Dostęp otwarty
• Prawa: https://creativecommons.org/licenses/by-nc-sa/4.0 ;
  Prawa autorskie (c) 2024 Seweryn Ziajor, Justyna Tomasik, Piotr Sajdak, Mikołaj Turski, Artur Bednarski, Marcel Stodolak, Łukasz Szydłowski, Klaudia Żurowska, Aleksandra Krużel, Kamil Kłos, Marika Dębik
• Data:
  2024.04.11
• Typ:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Język:
  angielski
• Źródło:
  Journal of Education, Health and Sport; Vol. 65 (2024); 11-27;
  Journal of Education, Health and Sport; Tom 65 (2024); 11-27;
  2391-8306
• Format:
  application/pdf
• Wydawca:
  Nicolaus Copernicus University, Toruń, Poland
• Relacja: https://apcz.umk.pl/JEHS/article/view/49795/38263
• Temat i słowa kluczowe:
  Artificial Intelligence;
  machine learning;
  deep learning;
  diabetes
• Identyfikator: https://apcz.umk.pl/JEHS/article/view/49795
• Dostawca danych:
  Akademicka Platforma Czasopism
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