Development of an augmented reality (AR) glasses system for medical safety measures using artificial intelligence (AI)
Year: 2024
Type: article
Abstract: Medical errors are not generally disclosed but they do happen and vary in severity. By prioritising patient safety, Dr Kohei Tanaka, a clinical engineer from the Tokyo University of Technology, wants to find a solution for preventing medical errors or, at the very least, ensuring they don’t escalate into serious incidents. The solution Tanaka is working on combines artificial intelligence (AI) with augmented reality (AR) glasses. The idea is that the glasses will provide medical professionals with accurate and reliable information at critical times. By providing them with rapid access to real-time data, the expectation is that this will reduce critical errors. As part of the validation process, newly qualified nurses will compare current training methods with training using his AR glasses and also complete a questionnaire on the viability of the glasses. So far, the researchers have been able to verify the retention of data provided through the glasses for a period of about three months and believe large-scale verification will reveal the mechanism of long-term memory in the future. By providing accurate, up-to-date information in a visual (still images and video) and auditory format, the glasses assist with the rapid delivery of critical data. However, there remain question marks regarding how much data can be effectively presented to the operator at the point of use as information overloading is to be avoided. The system included a voice-synthesised text reading guide as well as the display through the bone conduction earpiece within the device, which means the user can continue with the procedure while listening to the voice guide, reading reliance on visuals and thereby increasing safety.
Source: Impact
Author: Kohei Tanaka
Institution: Tokyo University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404087315