医院布局大模型很热闹，缘何还难以真正落地

Core Viewpoint - The integration of AI in hospitals is a complex process that requires time and careful implementation, with current deployments not meeting expectations [1][4]. Group 1: AI Implementation Challenges - Many hospitals are experiencing difficulties in effectively utilizing AI technologies like DeepSeek, with some healthcare workers reporting they are either not using it or are uncomfortable with it [1][4]. - The transition to AI in healthcare is not instantaneous; hospitals need to adopt a phased approach, starting with select departments and iterating on applications over time [4][6]. Group 2: Cost and Accessibility - The cost of developing AI solutions has significantly decreased, from several million yuan to around 5 million yuan, making it more accessible for hospitals [5]. - Each hospital has the flexibility to decide when to adopt AI technologies, rather than feeling pressured to implement them all at once [5]. Group 3: Areas of Exploration - Current explorations of AI in hospitals focus on improving patient experience, enhancing medical services, and optimizing hospital management [6]. - The most significant challenge remains the application of AI in medical services, as trust in AI's capabilities is still developing [6][7]. Group 4: Data Quality and Standards - The quality and standardization of data are crucial for the development of effective medical AI models, with many hospitals struggling to achieve true big data status due to inconsistencies in data collection [6]. - The terminology used in medical contexts can vary significantly, complicating data analysis and reporting [6]. Group 5: Future Outlook - There is optimism regarding the future of AI in healthcare, with advancements in AI technology expected to enhance its capabilities in clinical decision-making [7]. - The ongoing evolution of AI is anticipated to lead to deeper integration into clinical practices, despite current limitations [7].