We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks. We evaluate several LLMs, including Meditron, Llama2, and Mistral, to analyze the ability of these models to interact effectively with physicians across different scenarios. We consider questions from PubMedQA and several tasks, ranging from binary (yes/no) responses to long answer generation, where the answer of the model is produced after an interaction with a physician. Our findings suggest that prompt design significantly influences the downstream accuracy of LLMs and that LLMs can provide valuable feedback to physicians, challenging incorrect diagnoses and contributing to more accurate decision-making. For example, when the physician is accurate 38% of the time, Mistral can produce the correct answer, improving accuracy up to 74% depending on the prompt being used, while Llama2 and Meditron models exhibit greater sensitivity to prompt choice. Our analysis also uncovers the challenges of ensuring that LLM-generated suggestions are pertinent and useful, emphasizing the need for further research in this area.

AI algorithms can quickly detect and localize wrist fractures in X-ray images, which can augment the work of harried emergency physicians and radiologists. Missing a fracture on an emergency department radiograph is one of the most common causes of diagnostic errors and subsequent litigation. Such errors are due to clinical inexperience, distraction, fatigue, poor viewing conditions and time pressures. The study authors, from the National University of Singapore, hypothesized that automated analysis using artificial intelligence (AI) would be "invaluable" in reducing these misreadings and that an object detection convolutional neural network (CNN) would work better than other CNNs. Object detection CNNs are extensions of image classification models that not only recognize and classify objects on images, but also localize the position of each object.

The FDA has approved a new artificial intelligence tool called OsteoDetect that helps doctors diagnose wrist fractures. The tool is a computer-aided detection and diagnosis software application that uses AI algorithms to help healthcare providers determine if a wrist fracture is present at a faster rate than traditional diagnostic technologies. The FDA has increasingly approved new technologies that offer novel ways to diagnose and support healthcare providers. The new OsteoDetect approval is the latest example of the FDA's increased acceptance of new technologies, this one specifically targeted at diagnostics. The software works by using AI to analyze 2D x-ray images of the patient's wrist.

The U.S. Food and Drug Administration (FDA) has just approved an AI-based diagnostic tool that can accurately detect wrist fractures. Imagene's OsteoDetect uses machine learning algorithms to study 2D X-rays for the signs of wrist fractures. "Artificial intelligence algorithms have tremendous potential to help health care providers diagnose and treat medical conditions," said Robert Ochs, Ph.D., acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health. "This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures." OsteoDetect isn't about to replace doctors but it can help improve fracture detection and get the correct diagnosis and treatment quickly.

The FDA has been approving its fair share of AI-powered medical technology, but its latest might be particularly helpful if you ever have a nasty fall. The agency has greenlit Imagen's OsteoDetect, an AI-based diagnostic tool that can quickly detect distal radius wrist fractures. Its machine learning algorithm studies 2D X-rays for the telltale signs of fractures and marks them for closer study. It's not a replacement for doctors or clinicians, the FDA stressed -- rather, it's to improve their detection and get the right treatment that much sooner. The approval came relatively quickly by using the De Novo premarket review pathway, which streamlines the process for products with "low to moderate risk."

The Food and Drug Administration has cleared new computer-aided detection and diagnosis software that uses an artificial intelligence algorithm to analyze X-ray images to detect wrist fractures in adult patients. The OsteoDetect software from Imagen Technologies, which was reviewed through the De Novo premarket regulatory pathway for low to moderate risk devices, analyzes wrist radiographs using machine learning techniques to identify and highlight regions of distal radius fracture--a common type of wrist fracture--to aid detection and diagnosis. "Artificial intelligence algorithms have tremendous potential to help healthcare providers diagnose and treat medical conditions," said Robert Ochs, acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health. "This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures." According to the FDA, the software is "intended to be used by clinicians in various settings, including primary care, emergency medicine, urgent care and specialty care, such as orthopedics."

Today, the U.S. Food and Drug Administration permitted marketing of Imagen OsteoDetect, a type of computer-aided detection and diagnosis software designed to detect wrist fractures in adult patients. "Artificial intelligence algorithms have tremendous potential to help health care providers diagnose and treat medical conditions," said Robert Ochs, Ph.D., acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health. "This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures." The OsteoDetect software is a computer-aided detection and diagnostic software that uses an artificial intelligence algorithm to analyze two-dimensional X-ray images for signs of distal radius fracture, a common type of wrist fracture. The software marks the location of the fracture on the image to aid the provider in detection and diagnosis.