Current neurology and neuroscience reports
40343612
PURPOSE OF REVIEW: Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome...
BACKGROUND: The objective of this study was to evaluate the concordance between therapeutic recommendations proposed by a multidisciplinary team meeting and those generated by a large language model (ChatGPT) for colorectal cancer. Although multidisc...
Generative Pre-trained Transformer (GPT)-4, a versatile conversational artificial intelligence, has potential applications in medicine, but its ability to support physicians' decision-making remains unclear. We evaluated GPT-4's performance in assist...
BACKGROUND: Artificial intelligence (AI) chatbots are excellent at generating language. The growing use of generative AI large language models (LLMs) in healthcare and dentistry, including endodontics, raises questions about their accuracy. The poten...
To assess the diagnostic and treatment decision-making accuracy of ChatGPT for various dental problems in pediatric patients compared to specialized pediatric dentists. This study included 12 cases, each with an average of three dental problems, re...
Advancements in machine learning have revolutionized preoperative risk assessment. In this article, we comment on the article by Huang , which presents a recent multicenter cohort study demonstrated that machine learning algorithms effectively strati...
The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose significant concerns. This study investigates potential biases in chatbot-generated recommendations ...
INTRODUCTION: The provision of optimal care for older adults with complex chronic conditions (CCCs) poses significant challenges due to the interplay of multiple medical, pharmacological, functional and psychosocial factors. To address these challeng...
BACKGROUND: This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, partic...
Clinical event extraction is crucial for structuring medical data, supporting clinical decision-making, and enabling other intelligent healthcare services. Traditional approaches for clinical event extraction often use pipeline-based methods to ident...