Diagnostic Accuracy

Diagnostic accuracy research focuses on improving the reliability and efficiency of disease detection, primarily through the integration of artificial intelligence (AI) into clinical workflows. Current efforts center on developing and validating AI models, including convolutional neural networks (CNNs), vision transformers, and large language models (LLMs), for various medical imaging modalities and applications, often incorporating explainable AI (XAI) techniques to enhance trust and transparency. These advancements hold significant potential to improve diagnostic speed and accuracy, reduce human error, and ultimately enhance patient care, though rigorous validation and addressing issues like adversarial attacks and bias remain crucial.