Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using Retrieval-Augmented Generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 68 lab tests--30 with conditional factors and 38 without. For tests with factors, normal ranges depend on patient-specific information. Our results show GPT-4-turbo with RAG achieved a 0.95 F1 score for factor retrieval and 0.993 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 29.1% in factor retrieval and showed 60.9% and 52.9% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results. Introduction The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 played a key role in promoting the adoption and meaningful use of electronic health records (EHRs) throughout the U.S. healthcare system. Through the Medicare and Medicaid EHR Incentive Programs, the Act provided financial incentives that facilitated widespread EHR adoption.

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code.

"So I started to scope building one," he said. The result is Champions Place, a residence for young people with physical disabilities that doubles as a user-research center for companies developing accessible products. The facility in Johns Creek, Ga., opened in October to its first 14 residents, six years after Mr. Thompson co-founded the nonprofit Champions Community Foundation to begin financing the project. The space aims to be fully accessible for people with conditions such as spina bifida and cerebral palsy, who often rely on a wheelchair. Alongside providing a shared home for residents, Champions Place aims to be a social hub for members of the Titans, the 80-person strong wheelchair sports group Mr. Thompson created with five other families in 2009.

Artificial intelligence and machine learning are quickly becoming an integral part of healthcare delivery. Both on the clinical care and operational side of healthcare organizations, AI has is powering technology that keeps patients safe and improves efficiency for the revenue cycle, supply chain and more. Here are 100-plus companies in the healthcare space using artificial intelligence. To add a company to this list, contact Laura Dyrda at ldyrda@beckershealthcare.com. AiCure is an AI and advanced data analytics company that uses video, audio and behavioral data to better understand the connection between patients, disease and treatment. It allows physicians to have access to clinical and patient insights. Aiva Health developed Aiva, the first voice-powered care assistant.

Artificial intelligence--a broad set of technologies that enable machines to mimic the human brain's ability to process information, learn and adapt--holds potential in healthcare to improve patient outcomes and reduce costs, but it hasn't yet been widely adopted in daily clinical practice. However, some leading healthcare organizations, such as the Cleveland Clinic, Intermountain Healthcare and others, are beginning to build the infrastructure and data science capabilities to use AI to deliver clinical and financial benefits. While some industries are using AI programs designed to recognize speech, written language or visual data or do problem-solving, health systems are gaining experience with machine learning, a subset of AI focused on finding patterns or relationships in data in an iterative, or learning, fashion. Early projects have demonstrated promising results. In some of these cases, healthcare organizations have purchased a commercial tool to help them reach a specific clinical goal, such as reducing hospital readmission rates or predicting which patients are at highest risk of becoming expensive cases.