Shanghai is emerging as a hub for artificial intelligence applications in healthcare, with new technologies assisting clinicians across multiple specialties while extending their reach to schools, communities, and households.
The recently launched “Qizhi” AI model represents a breakthrough in pediatric brain health. Developed through collaboration between Fudan University, its affiliated Children’s Hospital, and technology partners, this innovation enables intelligent, precise diagnosis by analyzing uploaded test results to identify abnormalities and provide diagnostic clues – particularly valuable for primary care physicians.
Cardiology Advances with AI
At the concluded 19th Oriental Congress of Cardiology (OCC 2025), the CardioMind model demonstrated remarkable capabilities in multimodal data integration and complex diagnosis. The system, trained on extensive medical data with continuous daily updates, showcased its diagnostic precision through a simulated consultation featuring a digital avatar of academician Ge Junbo interacting with a standardized patient.
Onco-Cardiology Breakthrough
The newly formed “Onco-Cardiology AI Consortium” addresses critical challenges in treating immunotherapy-induced myocarditis in cancer patients. Fudan University Zhongshan Hospital’s multidisciplinary team developed this solution after four years of research, integrating novel biomarkers to clarify the complex relationship between immunotherapy and cardiac injury. Academician Ning Guang emphasizes AI’s potential to optimize medical resource allocation and improve accessibility.
Expanding Beyond Hospital Walls
AI’s healthcare applications now extend far beyond clinical settings. The Qizhi model serves as a prime example, enabling brain health assessments in both home and school environments. According to Dr. Wang Yi, director of Fudan Children’s Hospital, expert digital avatars are making technology more approachable.
Shanghai’s first AI ophthalmic assistant, “BrightEye Guardian,” debuted during Children’s Day activities, providing instant answers to vision care questions. Meanwhile, the Onco-Cardiology team is developing a hospital-to-home monitoring network for high-risk patients.
Challenges and Considerations
While AI promises more efficient and accessible healthcare, Academician Fan Jia cautions that model reliability depends entirely on training data quality. Key challenges include establishing patient trust in digital doctors and ensuring compliance with ethical and regulatory standards – crucial hurdles for medical AI applications to overcome.
As these technologies evolve, the integration of professional knowledge bases with AI promises cost-effective, precise medical services across multiple scenarios, with familiar doctors’ digital avatars adding a human touch to technological advancement.
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