Gastric carcinoma(GC),a highly prevalent malignant tumor globally,often progresses to advanced stages by the time of diagnosis due to its insidious clinical presentation,thereby significantly reducing therapeutic effectiveness and patient quality of life.Accurate screening and histopathological characterization of early gastric cancer(EGC)are essential for developing individualized treatment approaches.Although endoscopic techniques remain the gold standard for early GC detection,their diagnostic accuracy is largely dependent on the operator's skill,a challenge that current artificial intelligence(AI)-assisted innovations aim to address by stan-dardizing diagnostic procedures.Deep learning(DL)-based computer vision systems have demonstrated remarkable performance in identifying subtle EGC features,not only improving lesion detection sensitivity but also enabling automated assessment of key pathological indicators.These technological advances offer objective,visualized diag-nostic support for clinical decision-making.This review provides a systematic overview of recent developments in DL applications for endoscopic image analysis of EGC and evaluates their potential for clinical integration.

Gastric carcinoma(GC),a highly prevalent malignant tumor globally,often progresses to advanced stages by the time of diagnosis due to its insidious clinical presentation,thereby significantly reducing therapeutic effectiveness and patient quality of life.Accurate screening and histopathological characterization of early gastric cancer(EGC)are essential for developing individualized treatment approaches.Although endoscopic techniques remain the gold standard for early GC detection,their diagnostic accuracy is largely dependent on the operator's skill,a challenge that current artificial intelligence(AI)-assisted innovations aim to address by stan-dardizing diagnostic procedures.Deep learning(DL)-based computer vision systems have demonstrated remarkable performance in identifying subtle EGC features,not only improving lesion detection sensitivity but also enabling automated assessment of key pathological indicators.These technological advances offer objective,visualized diag-nostic support for clinical decision-making.This review provides a systematic overview of recent developments in DL applications for endoscopic image analysis of EGC and evaluates their potential for clinical integration.

Strengthening the supervision over the use of large-scale medical equipment is an effective means to improve the efficiency of equipment use and the quality of medical services, and it is an important part of promoting the construction of the Healthy China and the development of health undertakings. Through four stages of preliminary demand investigation, intelligent collection of data, intelligent analysis and evaluation, and continuous improvement, a large-scale medical equipment intelligent management platform was built in our hospital. Real-time data collection, interconnection, analysis and evaluation were achieved, which could help the use and supervision, improve efficiency and effectiveness, and optimize the evaluation system.

As an important part of public emergency response mechanism, medical supplies and equipment support is an important material support for medical rescue and disposal.For the medical equipment management industry, the epidemic prevention and control practice is a major test, but also a leap forward development opportunity. Based on the working experience, the authors put forward that medical equipment support should be upgraded to the strategic material support for the prevention and control of major infectious diseases. It is suggested to improve the whole process support management system of medical equipment purchase, collection, allocation and donation. The authors discuss how to implement the construction of medical equipment support elements from four aspects of system, technology, supply chain and information system, and also discuss how to promote the development of medical engineering interdisciplinary.