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…
Continue Reading
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…
Continue Reading
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…
Continue Reading
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…
Continue Reading
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…
Continue Reading
The neonatal period is critical and stressful, particularly in low-resource settings where existing monitoring methods for newborns are sporadic and labor-intensive. There is a pressing need for an affordable, user-friendly device that offers continuous…
Continue Reading
The rapid development of AI solutions reveals opportunities to address the underdiagnosis and poor management of chronic conditions in developing settings. Using the method of simulated patients and experimental designs, we evaluate the quality,…
Continue Reading
Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing AI models often requires extensive annotation, and existing models tend to…
Continue Reading
We developed a deep residual network (ResNet) framework to classify thyroid cancer differentiation states by integrating multiomic data and interpretability analysis. Our framework incorporated untargeted metabolomic, whole-exome sequencing, and transcriptomic data from 158 thyroid…
Continue Reading
Deep learning has proven to be an excellent tool for interpreting medical images in adults and children. However, it is still underdeveloped for neonates. This study developed NeoCLIP, a novel deep contrastive learning model…
Continue Reading
