OBJECTIVE: To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs) in radiologic image-based visual question answering (VQA).... read more
Synthetic data, generated through advanced artificial intelligence models, are gaining traction in healthcare research, particularly in high-stakes fields such as haematology and oncology. By replicating statistical properties, intervariable relation... read more
The clinical adoption of artificial intelligence (AI) has focused on enabling automation, but conventional accuracy metrics fail to answer a key question: when is it safe to trust an AI system? We introduce the Safety-Aware Receiver Operating Charact... read more
Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains. Recent studies have highlighted the potential of using neural data to mimic brain processing; howe... read more
Novel advances in healthcare-related Internet of Things (IoT) systems have recently had significant impacts on clinical decision-support systems (CDSS) and patient health monitoring. Securing networks using conventional cybersecurity models becomes i... read more
Artificial intelligence (AI) offers objective, adaptive tools for skill enhancement in microsurgical training, but evidence is fragmented. This systematic review evaluates AI-enhanced training efficacy compared to traditional methods, focusing on tec... read more
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