Latest AI and machine learning research in diabetes for healthcare professionals.
Background Gestational diabetes mellitus (GDM) affects 1-in-7 pregnancies globally and is associated with significant short- and long-term health consequences. Although health behaviour change interventions can effectively reduce these risks, a significant implementation gap exists in translating this evidence into routine practice. Bump2Baby and Me (B2B&Me) was a mobile health (mHealth) coaching ...
Medical image classifiers are often trained within one source population, yet clinical deployment requires robustness to patients whose appearance, acquisition style, and disease prevalence differ from the source cohort. Existing fairness and robustness methods often require group supervision or treat appearance variation as an undifferentiated nuisance, which is insufficient when population-corre...
The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large la...
Background. Retinopathy of prematurity (ROP) is a preventable cause of childhood blindness, with rising burden in low- and middle-income countries whe...
Background: Poor glycemic control is associated with cardiovascular disease (CVD) risk. However, it is unknown whether glycemic control is related to ...
Diabetic retinopathy (DR) is a local retinal lesion process and a visible manifestation of systemic microvascular injury. Modern retinal AI can grade ...
Endometrial cancer (EC) incidence is closely linked to metabolic and hormonal factors. The TyGFI, a composite indicator integrating the triglyceride-g...
While Photoplethysmography (PPG) is established as a noninvasive optical tool for monitoring heart rate and oxygen saturation, its high-resolution blo...
Chemotherapy-induced peripheral neuropathy (CIPN) is a common and painful side effect of paclitaxel (PTX) treatment. The most common measures of painf...
Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demog...
Abstract Background: Disability prediction in elderly with cardiometabolic multimorbidity (CMM) is limited. We developed a dynamic nomogram and addres...
Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series e...
Generative AI tools such as ChatGPT are increasingly used by the public to seek guidance on diet and physical activity for type 2 diabetes (T2D) preve...
Diagnosed diabetes affects approximately 38.4 million Americans, but its burden is not evenly distributed across U.S. counties. Existing machine-learn...
Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requ...
Alzheimer's Disease is a chronic neurodegenerative disorder projected to affect 115 million people by 2050, driven by mechanisms like the cholinergic ...
Background: Although diabetes is a potent risk factor for the development of peripheral artery disease (PAD), the effect of cumulative metabolic expos...
Purpose: To investigate how artificial intelligence (AI) systems detect referrable diabetic retinopathy (DR) from retinal photographs by analysing hea...
Offline reinforcement learning (ORL) offers the potential to improve the quality of clinical decision-making using historical electronic health record...
Pharmacological interventions targeting the biological processes of ageing hold significant potential to extend healthspan and promote longevity. This...