Diabetes mellitus is a major global health challenge, affecting over half a billion adults worldwide with prevalence projected to rise. Although the American Diabetes Association (ADA) provides clear diagnostic thresholds, early recognition remains difficult due to vague symptoms, borderline laboratory values, gestational complexity, and the demands of long-term monitoring. Advances in large language models (LLMs) offer opportunities to enhance decision support through structured, interpretable, and patient-friendly outputs. This study evaluates GPT-5, the latest generative pre-trained transformer, using a simulation framework built entirely on synthetic cases aligned with ADA Standards of Care 2025 and inspired by public datasets including NHANES, Pima Indians, EyePACS, and MIMIC-IV. Five representative scenarios were tested: symptom recognition, laboratory interpretation, gestational diabetes screening, remote monitoring, and multimodal complication detection. For each, GPT-5 classified cases, generated clinical rationales, produced patient explanations, and output structured JSON summaries. Results showed strong alignment with ADA-defined criteria, suggesting GPT-5 may function as a dual-purpose tool for clinicians and patients, while underscoring the importance of reproducible evaluation frameworks for responsibly assessing LLMs in healthcare.

Diabetes, resulting from inadequate insulin production or utilization, causes extensive harm to the body. Existing diagnostic methods are often invasive and come with drawbacks, such as cost constraints. Although there are machine learning models like Classwise k Nearest Neighbor (CkNN) and General Regression Neural Network (GRNN), they struggle with imbalanced data and result in under-performance. Leveraging advancements in sensor technology and machine learning, we propose a non-invasive diabetes diagnosis using a Back Propagation Neural Network (BPNN) with batch normalization, incorporating data re-sampling and normalization for class balancing. Our method addresses existing challenges such as limited performance associated with traditional machine learning. Experimental results on three datasets show significant improvements in overall accuracy, sensitivity, and specificity compared to traditional methods. Notably, we achieve accuracies of 89.81% in Pima diabetes dataset, 75.49% in CDC BRFSS2015 dataset, and 95.28% in Mesra Diabetes dataset. This underscores the potential of deep learning models for robust diabetes diagnosis. See project website https://steve-zeyu-zhang.github.io/DiabetesDiagnosis/

Bethesda (Maryland) [US], April 16 (ANI): A new study has found that a fully-automated artificial intelligence (AI) deep learning model can identify early signs of type 2 diabetes on abdominal CT scans. The findings of the study were published in the journal, 'Radiology'. Type 2 diabetes affects approximately 13 per cent of all U.S. adults and an additional 34.5 per cent of adults meet the criteria for pre-diabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of pre-diabetes can last up to 8 years and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease.

Bethesda (Maryland) [US], April 16 (ANI): A new study has found that a fully-automated artificial intelligence (AI) deep learning model can identify early signs of type 2 diabetes on abdominal CT scans. For this retrospective study, Dr Summers and colleagues, in close collaboration with co-senior author Perry J. Pickhardt, M.D., professor of radiology at the University of Wisconsin School of Medicine & Public Health, used a dataset of patients who had undergone routine colorectal cancer screening with CT at the University of Wisconsin Hospital and Clinics. The deep learning model displayed excellent results, demonstrating virtually no difference compared to manual analysis. In addition to the various pancreatic features, the model also analyzed the visceral fat, density and volumes of the surrounding abdominal muscles and organs. The best predictor's type 2 diabetes in the final model included intrapancreatic fat percentage, pancreas fractal dimension, plaque severity between the L1-L4 vertebra level, average liver CT attenuation, and BMI.

Bethesda (Maryland) [US], April 12 (ANI): According to a new study, researchers using a fully-automated artificial intelligence (AI) deep learning model were able to identify early signs of diabetes" type 2 diabetes on abdominal CT scans. The study was published in the journal, 'Radiology'. Type 2 diabetes affects approximately 13 per cent of all U.S. adults and an additional 34.5 per cent of adults meet the criteria for prediabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of pre-diabetes can last up to 8 years and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease. Abdominal CT imaging can be a promising tool to diagnose diabetes" type 2 diabetes.

Bethesda: According to a new study, researchers using a fully-automated artificial intelligence (AI) deep learning model were able to identify early signs of type 2 diabetes on abdominal CT scans. The study was published in the journal, 'Radiology'. Type 2 diabetes affects approximately 13 per cent of all U.S. adults and an additional 34.5 per cent of adults meet the criteria for prediabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of pre-diabetes can last up to 8 years and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease.

Abdominal CT imaging can be a promising tool to diagnose type 2 diabetes. CT imaging is already widely used in clinical practices, and it can provide a significant amount of information about the pancreas. Previous studies have shown that patients with diabetes tend to accumulate more visceral fat and fat within the pancreas than non-diabetic patients. However, not much work has been done to study the liver, muscles, and blood vessels around the pancreas, said study co-senior author Ronald M. Summers, MD, PhD, senior investigator and staff radiologist at the National Institutes of Health Clinical Center in Bethesda, Maryland.

For many years, diabetes cases have largely been classified as either type 1 or type 2. But a new study suggests that there may actually be five different types of the disease--some of which may be more dangerous than others. A new classification system could help doctors identify the people most at risk for complications, the study authors say, and could pave the way for more personalized and effective treatments. The research article, published in The Lancet: Diabetes & Endocrinology, calls attention to the need for an updated diabetes classification system. The current system "has not been much updated during the past 20 years," the authors wrote in their paper, "and very few attempts have been made to explore heterogeneity of type 2 diabetes"--despite calls from expert groups over the years to do so. Meanwhile, they wrote, diabetes is the fastest-increasing disease worldwide, and existing treatments have been unable to stem the tide or prevent the development of chronic complications in many patients.