Cell综述：生成式AI，开启医学新时代

Core Viewpoint - The article discusses the transformative potential of artificial intelligence (AI) in the biomedical field, emphasizing advancements in large language models (LLMs) and multimodal AI that can enhance diagnostics, patient interactions, and medical predictions [2][6][11]. Group 1: Technological Innovations - Recent advancements in AI, particularly in LLMs and multimodal AI, are set to revolutionize the medical field by improving diagnostics and patient interactions [6]. - Key architectural innovations such as Transformer architecture, generative adversarial networks, and diffusion models have contributed to the development of complex generative AI systems [2][4]. Group 2: Medical Practice Transformation - AI-enabled medical practices are shifting clinical care from sporadic interactions to continuous monitoring and regular follow-ups, allowing for proactive healthcare in familiar environments [8]. - New medical knowledge can be more easily integrated into care models, and AI technologies are facilitating the development of new drugs [8]. Group 3: Multiscale Medical Predictions - AI algorithms can predict future medical events based on various dynamic inputs, applicable at multiple levels from molecular to population [10]. - The future of medicine will involve tools capable of processing vast amounts of information, significantly improving diagnostic accuracy and patient outcomes [11]. Group 4: Challenges and Implementation - Despite the promising advancements, the widespread clinical adoption of AI tools faces significant challenges, including bias, privacy concerns, regulatory hurdles, and integration with existing healthcare systems [6][11]. - Most AI tools are still in development, with few demonstrating clear benefits across all users or situations, which remains a major barrier to broader usage by healthcare professionals [11]. Group 5: Roadmap for AI Implementation - The roadmap for implementing medical AI involves transitioning from basic scientific research to concept validation models, leading to larger models and early clinical applications that pave the way for final clinical deployment and optimization [14].