With the fast advancement of information technology and artificial intelligence, the conventional medical service model has been presented with new growth potential. Internet-based health care has become one of the unavoidable future delivery methods for diagnostic and therapeutic services. Internet-based hospitals are being deployed in medical facilities throughout. The extension of offline to online diagnosis and treatment will need new standards for the personal competency of physicians as well as new requirements for medical education and staff training. In the context of universal Internet diagnosis and treatment, research on the full-cycle training of medical talent will play a clear guiding role in the development of physicians’ skills. By evaluating the relevant literature on competence model and interviewing the behavior events of working physicians in e-hospitals, together with the real situation of current medical students and doctor training barriers, this article aims to improve the quality of remote healthcare via provide related path for enhancing the periodic medical education based on the competency variables.
As artificial intelligence (AI) becomes increasingly integrated into mainstream healthcare applications, there has been a surge in the number of AI-centered diagnostic test accuracy studies. In September 2025, the international journal Nature Medicine published the Reporting Guideline for Diagnostic Accuracy Studies Using AI (STARD-AI). The guideline represents a comprehensive reporting framework specifically updated for AI-based studies within the Standards for Reporting Diagnostic Accuracy Studies (STARD). It consists of a main checklist (40 major items with 48 sub-items) and an abstract checklist (11 items). This article provides an interpretation of STARD-AI's development process, main content, scope of application, and specific item details, aiming to assist researchers, clinicians, and editors in thoroughly understanding and correctly applying STARD-AI. The goal is to enhance the quality and transparency of reporting in AI-centered diagnostic accuracy studies and to promote their standardized and ethical integration into the medical field.
