1QBit earns second place at the (Society for Imaging Informatics in Medicine) SIIM Hackathon for the prototype of their pAI project. The prototype involved implementing XrAI on a Raspberry Pi—a small single-board computer developed with the intention of teaching basic computer science in schools and in developing countries. The demonstration is a great leap forward in providing greater accessibility to assisted medical imaging, and a powerful educational tool for underserved communities that lack internet access and financial or medical resources.
Your Guide to the World of Quantum
Artificial intelligence (AI) is revolutionizing radiology by speeding up workflows and increasing the accuracy of abnormality detection in X-ray imaging. This article highlights four main steps required in applying deep learning—a type of AI—to augment radiology.
Natural language processing (NLP) is a family of techniques that often use machine learning to analyze, process, and generate natural language text. It enables computers to process human language and derive meaning from natural language. For example, NLP-driven software can translate text from one language to another. It is also used by search engines to enhance the search experience by suggesting commonly used search terms and subsequent words in common phrases. Techniques that use NLP have also found applications in radiology, enabling the automatic identification and extraction of information from radiologists’ reports.
Radiology reports often contain follow-up recommendations for further diagnosis and to assist in the evaluation of potentially serious diseases. Over 35% of follow-up imaging recommendations fail to be scheduled.¹ If these recommendations are not tracked in a timely manner, or lost track of entirely, they can damage doctor–patient relationships and result in delayed diagnosis and poor patient outcomes.
At the 106th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), 1QBit showcased XrAI, an AI-powered clinical support and workflow solution with the potential to set a new standard for world-class health care and transform the practice of radiology. This solution empowers frontline clinicians to improve their capability to identify abnormalities and provide better patient care, especially in remote communities where radiology expertise is not available.
Blood pressure diseases have increasingly been identified as being among the main factors threatening human health.¹ Measuring and monitoring blood pressure regularly is important for the early detection and diagnosis of diseases related to blood pressure and to ensure timely treatment and prevention. Medical devices using algorithms enhanced by machine learning can improve blood pressure measurements. Such devices are the subject of active research at the intersection between health care and machine learning.