Sequential Electrocardiography Changes
This case report describes a woman in her 60s with sequential type I Kounis syndrome and cholecystocardial syndrome, leading to dynamic, reversible electrocardiography changes mimicking acute coronary syndrome.
ABMS Lifelong Learning CME Activity
Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral
Importance Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. Objective To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets…
Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes
Importance Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control. Objective To examine whether a voice-based conversational artificial intelligence (AI) application…
Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes – A Randomized Clinical Trial
Importance Type 2 diabetes (T2D) is one of the most prevalent chronic diseases in the world. Insulin titration for glycemic control in T2D is crucial but limited by the lack of personalized and real-time tools. Objective To examine whether an artificial intelligence–based…
Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images
Importance Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. Objective To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images. Design, Setting and…
Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy
Objective To assess the cost-effectiveness of artificial intelligence (AI) for supporting clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology. Importance AI has been referred to as a facilitator for more precise, personalized, and safer health care, and AI algorithms…
Diabetic Retinopathy Screening Among Federally Qualified Health Center Patients Using Point-of-Care AI – DRES-POCAI: A Trial Protocol
Importance Diabetic retinopathy screening (DRS) rates have historically been low among underserved populations due to barriers in accessing traditional eye care. Although artificial intelligence (AI)–powered DRS provides a potential strategy to improve screening rates, its optimal integration into primary care workflows…
Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone
Importance Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. Objective To evaluate the performance of…
Dupilumab-Associated Ocular Surface Disease
This case report describes a man in his 30s who presented for evaluation of a 3-week history of irritation, redness, and photophobia in both eyes.
Planetary Health Diet and Risk of Cardiometabolic Diseases Among Women With Gestational Diabetes
Importance The Planetary Health Diet (PHD) integrates health and environmental sustainability, yet its association with chronic disease outcomes remains underexplored, particularly among women with a history of gestational diabetes (GD), who are at elevated risk of type 2 diabetes (T2D) and…
