打通AI医疗落地的“最后一公里”

Core Insights - The new generation of artificial intelligence (AI) technology, represented by large models, shows significant potential and application value in the healthcare sector, particularly in medical imaging interpretation, disease risk warning, and assisted diagnosis decision-making [1][2] - The integration of AI in healthcare is crucial for optimizing clinical diagnosis models and addressing the uneven distribution of medical resources, ultimately benefiting public health [1][2] Group 1: Current Challenges - There are significant barriers to the circulation of medical data, with the "data island" phenomenon hindering inter-institutional and inter-regional data connectivity, which is essential for AI model training [2][3] - The evaluation system for clinical applications of algorithms is underdeveloped, with a lack of authoritative clinical evaluation standards and dynamic regulatory frameworks for AI-assisted diagnostic tools [2][3] - Ethical governance frameworks need to be proactively established to address new ethical challenges arising from AI's deep involvement in clinical decision-making [2][4] Group 2: Proposed Solutions - A new national health data governance system should be established, focusing on unified, open, and interoperable medical data standards to eliminate data barriers between institutions [3][4] - A comprehensive clinical evaluation and regulatory mechanism covering the entire lifecycle of AI medical products should be developed, including guidelines for research, testing, approval, and monitoring [3][4] - A forward-looking ethical governance paradigm for AI in healthcare should be constructed, including guidelines for ethical review and algorithm governance to ensure transparency and fairness in AI applications [4][5]