Core Viewpoint - The integration of AI in healthcare is rapidly advancing, with significant potential benefits in diagnostics and treatment, but it also raises concerns about accuracy, practicality, and the impact on medical professionals' skills [1][5][9]. Group 1: AI Applications in Healthcare - AI is being utilized in various medical applications such as diagnostic assistance, medical record writing, and pre-consultation, with ongoing developments supported by government initiatives [1][2]. - The average accuracy of prenatal diagnosis for congenital defects is only 30%-60%, indicating a significant opportunity for AI to improve outcomes in cases like congenital diaphragmatic hernia [2][7]. - AI has shown high efficiency in areas like image recognition, pathology interpretation, and chronic disease management, which can reduce diagnostic time and human error [3][9]. Group 2: Concerns and Challenges - There are concerns that reliance on AI may lead to a decline in the diagnostic skills of younger doctors, as they may become overly dependent on AI tools [5][6]. - The accuracy of AI is not guaranteed, and it is essential for doctors to maintain their diagnostic authority and critical thinking when using AI [5][6]. - Challenges include the lack of a mature reimbursement system for AI-assisted diagnostics and the need for funding and support for AI development in healthcare [8][9]. Group 3: Regulatory and Development Recommendations - Recommendations include establishing a clinical access and evaluation system for AI medical products, ensuring safety and efficacy in real-world applications [9][10]. - It is suggested that pilot programs be initiated in community health centers and secondary hospitals to demonstrate the effectiveness of AI applications in healthcare [10]. - The government is encouraged to integrate AI into health city and digital governance initiatives to promote collaborative development among industry, hospitals, research, and regulation [10].