Core Viewpoint - The application and governance of AI in the healthcare sector are crucial topics, as AI is transforming the industry while also presenting various challenges that need to be addressed [2]. Group 1: AI's Impact on Healthcare - AI is enhancing various aspects of healthcare, including drug development, diagnostics, and personalized treatment plans, significantly improving efficiency and reducing costs [3][5]. - For instance, AI has increased the detection rate of cervical cancer screenings by 2-3 times in grassroots medical institutions and has reduced the time for predicting lung cancer gene mutations from weeks to just 1 minute [5]. - The traditional model of drug development, which typically takes 10 years and costs around $1 billion, is being transformed by AI, leading to faster and more effective drug discovery processes [5]. Group 2: Challenges and Risks of AI in Healthcare - Experts highlight that the risks associated with AI in healthcare primarily revolve around data flow, decision-making authority, and the reliability of AI systems [7]. - Data privacy is a significant concern, as healthcare data is highly sensitive, necessitating a balance between data security and accessibility [7]. - The complexity of obtaining original data for research poses challenges, as there are numerous policies and rules that practitioners must navigate [7]. Group 3: Reliability and Ethical Considerations - The reliability of AI systems is a pressing issue, particularly given the opaque nature of large models, which can produce unreliable outputs [8]. - There is a need for increased transparency in AI operations to ensure public trust and understanding of how AI systems function [8]. - The ethical implications of AI in healthcare are significant, as AI cannot fully replace human judgment, especially in complex moral situations [8].