BACKGROUND/OBJECTIVE: This study aimed to evaluate the accuracy, comprehensiveness, and readability of responses generated by various Large Language Models (LLMs) (ChatGPT-3.5, Gemini, Claude 3, and GPT-4.0) in the clinical context of uveitis, utiliz...
OBJECTIVE: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.
MedSegBench is a comprehensive benchmark designed to evaluate deep learning models for medical image segmentation across a wide range of modalities. It covers a wide range of modalities, including 35 datasets with over 60,000 images from ultrasound, ...
OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, ...
Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from...
The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computati...
International journal of medical informatics
39708667
BACKGROUND: Machine Learning (ML) models often struggle to generalize effectively to data that deviates from the training distribution. This raises significant concerns about the reliability of real-world healthcare systems encountering such inputs k...
Eye disease detection has achieved significant advancements thanks to artificial intelligence (AI) techniques. However, the construction of high-accuracy predictive models still faces challenges, and one reason is the deficiency of the optimizer. Thi...
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational mod...
Journal of the American Medical Informatics Association : JAMIA
39602813
OBJECTIVES: The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a c...