Role of Artificial Intelligence in Diabetes Research Diagnosis and Prognosis: A Narrative Review

Abstract

The use of artificial intelligence (AI) has proven to be valuable and transformative in the treatment of diabetes. With the ability to process large amounts of data, AI can draw significant conclusions that improve the accuracy of diagnostics and prognostic decision-making. Machine learning and deep learning are the most commonly used technologies in the field, as they have made remarkable advancements due to enhanced computer speed and more resources for computation. A narrative review of the research on the use of AI in diabetes treatment is presented, demonstrating the critical relevance of precise diabetes diagnosis, and prognosis and investigating the potential for artificial intelligence to revolutionize this specialized medical profession. Insights from prestigious journals demonstrate artificial intelligence's effective application in identifying various types of diabetes by harnessing extensive data sets and advanced algorithms to improve decision-making in healthcare. The paper also looks at the medical implications of AI-driven diabetic assessment and prognosis, such as earlier detection, fewer errors in diagnosis, and better outcomes for patients. The combination of AI with innovative imaging technology improves diagnostic accuracy and equips healthcare professionals to make informed decisions. Despite significant progress in technology and medical science, the assessment highlights potential hurdles and restrictions in using machine learning in diabetes investigations and treatment. Artificial Intelligence, like any other discipline, has constraints, and understanding these constraints is critical for effective deployment in diabetic management. Integrating artificial intelligence into diabetes investigation and treatment has significant promise for increasing diabetes care. Disease forecasting algorithms for diabetes will see a huge boost in accuracy as the forecasting capacity of artificial intelligence is optimized with organized data and ample computational capacity. Addressing potential barriers and ensuring appropriate adoption, on the other hand, are critical for realizing the full promise of machine learning in diabetes studies and treating patients.