Prototype of a Military Medic Smartphone Medical Graphical User Interface for Use by Medics in Deployed Environments.

INTRODUCTION: Prompt and effective combat casualty care is essential for decreasing morbidity and mortality during military operations. Similarly, accurate documentation of injuries and treatments enables quality care, both in the immediate postinjury phase and the longer-term recovery. This article describes efforts to prototype a Military Medic Smartphone (MMS) for use by combat medics and other health care providers who work in austere environments. MATERIALS AND METHODS: The MMS design builds on previous electronic health record systems and is based on observations of medic workflows. It provides several functions including a compact yet efficient physiologic monitor, a communications device for telemedicine, a portable reference library, and a recorder of casualty care data from the point of injury rearward to advanced echelons of care. Apps and devices communicate using an open architecture to support different sensors and future expansions. RESULTS: The prototype MMS was field tested during live exercises to generate qualitative feedback from potential users, which provided significant guidance for future enhancements. CONCLUSIONS: The widespread deployment of this type of device will enable more effective health care, limit the impact of battlefield injuries, and save lives.

Artificial Intelligence for the Future Radiology Diagnostic Service.

Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.