Core Viewpoint - The introduction of AI in medical record systems is controversial, with concerns about its impact on the training of young doctors and the potential for AI errors [3][11][12] Group 1: AI in Medical Training - Zhang Wenhong, a prominent figure in infectious disease medicine, refuses to integrate AI into medical record systems, arguing it could disrupt the training of young doctors who need systematic training to diagnose diseases [3] - Some young doctors believe that traditional methods of medical education are outdated in the AI era, suggesting that AI can accelerate learning and enhance clinical skills [9] - The debate centers around whether AI can effectively replace the hands-on training traditionally provided by experienced doctors [6][9] Group 2: AI Applications in Healthcare - AI has been applied in various healthcare scenarios, with over 80 recommended applications outlined by the National Health Commission, including intelligent diagnostic assistance in medical imaging and clinical decision-making [4] - The most common application is in medical imaging, with 73 cases reported, followed by clinical decision support for specific diseases and intelligent medical record generation [4][5] - AI tools are reported to significantly improve efficiency in tasks such as medical record writing and patient history organization, reducing time spent on these activities [5][6] Group 3: Concerns About AI Errors - There are significant concerns regarding the potential for AI to make mistakes, particularly when young doctors may lack the experience to identify these errors [11][12] - Some AI systems have demonstrated lower error rates than human doctors in specialized fields, but issues remain, such as the inability to access external databases for real-time corrections [11][12] - The responsibility for AI errors is a critical issue, with the consensus that users must possess the knowledge to verify AI outputs [12]