Core Viewpoint - The integration of AI in healthcare is increasing, with applications such as AI consultations, AI-assisted surgeries, and AI diagnostic tools, raising questions about their effectiveness and reliability in meeting real healthcare needs [4][5][6]. Group 1: AI in Healthcare Applications - AI medical applications are becoming more prevalent in daily life, with tools like "AI consultations" and "AI-assisted surgeries" being introduced in hospitals [4][5]. - The "安诊儿" app allows patients to consult with AI doctors, providing tailored advice based on symptoms, which has shown to prepare patients for hospital visits [5][6]. - A report indicated that public hospitals in the city have implemented various AI applications in clinical diagnosis and hospital management, showcasing a range of AI use cases [7][8]. Group 2: Effectiveness and Limitations - While AI consultations can provide useful suggestions, there are concerns about their reliability, as evidenced by cases where misdiagnoses occurred due to generic AI responses [6][8]. - AI-assisted diagnostic systems embedded in hospital workflows demonstrate higher accuracy and reliability compared to standalone AI consultations, as they utilize professional data and rigorous validation [6][8]. - The AI imaging technology in hospitals has significantly improved the efficiency and quality of diagnostic reports, with accuracy rates exceeding 90% [9][10]. Group 3: Future of AI in Healthcare - The current landscape shows a disparity in AI application between large hospitals and grassroots healthcare institutions, with only 10.1% of primary care facilities utilizing AI diagnostic tools [11]. - The city plans to enhance AI infrastructure to reduce redundancy and improve resource allocation across healthcare institutions [8][11]. - By 2030, the goal is to achieve widespread implementation of AI-assisted applications in grassroots healthcare, promoting technologies like AI imaging and clinical decision support [12].