인공지능 기반 신생혈관성 나이관련황반변성 진단보조소프트웨어 사용자 인터페이스 개선 연구

Abstract

With the recent development of Software as a Medical Device (SaMD), the significance of reducing use errors in medical devices is increasing. In this study, we conducted a formative evaluation of Artificial Intelligence (AI)-based software for ophthalmic image detection and diagnosis. The software automatically displays the presence or absence of neovascular age-related macular degeneration using fundus images to assist medical personnel in making diagnostic decisions. We aimed to find ways to reduce the number of use errors in formative evaluations. For this purpose, we conducted usability testing by performing tasks based on use scenarios for usability formative evaluations with intended users in their intended use environments, and then modified the user interface to reduce use errors. The initial formative evaluation revealed errors in utilizing the capture and dragand- drop functions for uploading fundus images, checking analysis results, and perform logout procedures. To reduce use errors, we improved the function by relocating the capture icon, displaying a hand-shaped cursor during dragging, inserting a fundus position guide, and eliminating the drag-and-drop function. We have also enhanced the design to present analytics results in a more intuitive manner and added a separate logout button to reduce the risk of use error. As a result, the number of use errors in the formative evaluation decreased from six to one in the summative evaluation. The development of ophthalmic image detection and diagnosis assistant software that reflects these improvements is expected to enhance user safety, usability, and reduce use errors.