Core Viewpoint - The integration of AI in the healthcare sector is accelerating due to policy incentives and technological advancements, showcasing significant potential in areas such as disease prevention, health management, assisted diagnosis, and drug development [1][2][3] Group 1: Data Acquisition Challenges - The application of "AI + healthcare" heavily relies on high-quality data, but the inconsistency in data formats and standards across institutions leads to low levels of data sharing, creating "data silos" that hinder access to quality datasets necessary for training AI models [1] - The "Three-Year Action Plan for Data Elements × Healthcare" (2024-2026) aims to address the "data silo" issue by promoting the release of healthcare data value and expanding new data application models in smart healthcare [1] Group 2: Clinical Validity and Credibility - The essence of healthcare is practical science, requiring rigorous clinical validation for any technology. Currently, no AI-designed drug has successfully passed Phase II clinical trials, indicating that "AI + pharmaceuticals" will face significant challenges ahead [2] - Some AI-generated solutions lack logical rigor and alignment with clinical realities, which diminishes trust among doctors and patients. Enhancing algorithm interpretability and rigor, along with strict clinical research validation, is essential for AI healthcare products to gain acceptance [2] Group 3: Sustainable Commercialization Pathways - Mature "AI + healthcare" products must not only demonstrate clinical value but also establish sustainable business models. Many companies in "AI + assisted diagnosis" and "AI + pharmaceuticals" are currently unprofitable and rely on financing for survival [2] - Companies need to develop clear pricing mechanisms, create value assessment systems, and establish diversified payment models to facilitate industry growth [2][3]