Abstract The global prevalence of non-suicidal self-harm (NSSI) among adolescents is becoming increasingly severe. Traditional mental health services are struggling to meet the growing social demand due to limited resource allocation and service accessibility. The rapid development of artificial intelligence (AI) technology provides a new technological path. The article systematically reviews the research progress of AI technology in adolescent NSSI, demonstrating key technologies such as machine learning, natural language processing, and deep learning in predicting NSSI risk prediction, emotion recognition and online intervention for adolescents. However, challenges remain, including algorithm bias, data privacy protection, model interpretability and ethical decision making. Future research should focus on multi disciplinary collaborative cooperation based on artificial intelligence to build a safe, effective and sustainable digital psychological intervention system, so as to provide innovative strategies and technical support for the early warning and intervention of NSSI behavior in adolescents.

How to use family doctors as a starting point to improve the level of chronic disease management is still a ma-jor challenge in China.This study summarizes the current chronic disease management models,family doctor contract service models,and new digital health technologies in China.It expounds the connection between the three and explores the advantages of new digital health technologies in chronic disease management and family doctor contract services,aiming to provide a solid theoretical foundation for improving the level of chronic disease management.

How to use family doctors as a starting point to improve the level of chronic disease management is still a ma-jor challenge in China.This study summarizes the current chronic disease management models,family doctor contract service models,and new digital health technologies in China.It expounds the connection between the three and explores the advantages of new digital health technologies in chronic disease management and family doctor contract services,aiming to provide a solid theoretical foundation for improving the level of chronic disease management.

Objective:To explore the marking method for magnetically controlled capsule gastroscopy (MCCG) pictures with artificial intelligence (AI), so as to improve the work efficiency of endoscopist and to reduce the blind area of AI image reading.Methods:According to the consensus of MCCG, 24 parts of stomach in 14 775 pictures of MCCG from 35 subjects in Shenzhen Zifu Medical Technology Co., Ltd received MCCG from March to August, 2020 were marked by ten gastroenterologists and one developer of MCCG with medical background, the marking shape included rectangles and polygons. Among the ten gastroenterologists, three were senior endoscopist (the total number of gastroenteroscopy operations over 80 000, chief physician or associate chief physician), four were medium seniority endoscopist (the total number of gastroenteroscopy operations between 10 000 and 80 000, associate chief physician), and three were junior endoscopist (the total number of gastroenteroscopy operations less than 10 000, attending physician). The pictures of the same subject were pre-marked by two selected senior endoscopists with blind method, and the standard of marking with most appropriate coincidence rate was determined. The qualified marked pictures were automatically learn with AI deep learning method, and the learning results were fed back. Chi square test was used for statistical analysis.Results:According to the pre-marked results, the standard of coincidence rate for rectangular marking area was set as 50.0% and that for polygon marking area was 70.0%. The first correction for qualified rate was 39.0% (5 762/14 775). A total of 9 013 pictures were corrected. After repeated training and correction for one to five times, all pictures were qualified marked. The marking qualified rate of senior endoscopist partners was higher than that of partners of different qualifications (48.7%, 1 200/2 466 vs. 19.0%, 825/4 337), and the difference was statistically significant ( χ2=659.20, P<0.001). There was no statistically significant difference in the marking qualified rate between the senior endoscopist partners and partners of senior endoscopist and capsule developer (48.7%, 1 200/2 466 vs. 49.6%, 1 496/3 019; P>0.05). Conclusions:Establishment of AI marking method for MCCG can provide technical support for AI non-blind area reading, and AI non-blind area monitoring during the operation of MCCG.