Carotid atherosclerosis is a key predictor of cardiovascular disease (CVD), necessitating early detection. While foundation models (FMs) show promise in medical imaging, their optimal selection and fine-tuning strategies for classifying carotid atherosclerosis from retinal…
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Predicting multiple postoperative complications remains challenging in perioperative care. Current approaches often address complications individually, limiting the potential for integrated risk assessment. We developed and externally validated a scalable, interpretable, tree-based multitask learning model…
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Clinical assessments of Parkinson’s disease (PD) focus on structured motor tasks, potentially underestimating real-world mobility impairment. Digital mobility outcomes (DMOs) from wearable devices, offer a promising alternative, but their clinical value remains unclear. This…
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Over 25 million U.S. patients with a non-English language preference face unsafe care because discharge instructions and other materials are rarely translated in time. Advances in translation assisted by large language models can close…
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Neuropsychiatric disorders have complex causes and exhibit considerable individual variability as they develop over time. This suggests the need for a shift from a focus on observable clinical symptoms to a personalized trajectory monitoring…
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Healthcare communication faces unprecedented challenges as the healthcare workforce is increasingly faced with increased administrative burdens and reduced time with patients. Conversational agents powered by generative AI may offer a potential solution by collecting…
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Patients with diabetes are at increased risk of comorbid depression or anxiety, complicating their management. This study evaluated the performance of large language models (LLMs) in detecting these symptoms from secure patient messages. We…
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Clinical decision-making substantially impacts patients’ lives and their quality of life. However, the black-box nature of AI-powered clinical decision support systems (CDSSs) complicates the interpretation of how decisions are derived. Explainable AI (XAI) improves…
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Electronic health records (EHRs) contain rich longitudinal information for clinical decision-making, yet LLMs struggle to reason across patient timelines. We introduce TIMER (Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records), a method to…
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Globally, drug discovery and development programs are complex, multi-decade long and prohibitively expensive. Artificial intelligence (AI) and other digital health technologies have the potential to enhance and accelerate each stage of drug discovery and…
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