As augmented intelligence (AI) becomes increasingly integrated into clinical practice, physicians must navigate new ethical and operational challenges. AI tools can enhance diagnostic accuracy, streamline workflows, and support personalized care, while also introducing concerns related to reliability, bias, transparency, privacy,…
In this activity, you engage with a patient case and explore key ethics pearls from the AMA Code of Medical Ethics that guide surrogate selection, conflict management, and balancing prior preferences with current best interests.
This case report describes a man presenting with amnestic seizure events manifesting with a wind swirl pattern on density spectral array, resulting in a diagnosis of antiñ?-aminobutyric acid B (GABAB) receptor antibodyñassociated encephalitis.
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.
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…
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…
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…
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…
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…
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…
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