Average improvement across clinical decision workflows.
Smart Hospital
Xinhua Hospital builds an agent-driven smart hospital model.
SenseCare supports Xinhua Hospital across smart service, clinical diagnosis, research, remote collaboration and pediatric agent innovation.
Case Summary
Xinhua Hospital
As a core institution of the National Children's Medical Center, Xinhua Hospital works with SenseCare around DaYi medical large language model capability, multimodal perception and agent workflows. The collaboration covers five directions: smart top-level planning, smart diagnosis and treatment, smart service, smart research and smart medical alliance.
Liver and thoracic AI surgical planning.
Service, diagnosis, research, alliance and top-level planning.
The hospital built a patient-centered service chain with smart triage, pre-consultation, AR navigation and digital human accompaniment.
Clinical systems include emergency collaboration for stroke and chest pain, digital pathology transformation and surgical planning for liver, thoracic and congenital heart scenarios.
Average clinical decision efficiency improved by 30% across multidisciplinary AI-assisted workflows.
AI three-dimensional surgical planning efficiency for liver and thoracic surgery improved by 5x.
The AI children's general practitioner marks an original pediatric medical-agent scenario.
A five-part smart hospital architecture
Xinhua Hospital's work with SenseCare is not a single application project. It connects local agents, scenario integration and metaverse-style spatial services into a smart-hospital operating model.
DaYi medical large language model capability supports the hospital's core workflows, while smart triage, pre-consultation, AR navigation and digital human service reshape the patient journey from passive wayfinding to active service delivery.
Clinical and research capability together
The clinical layer includes multi-disease AI-assisted diagnosis, emergency collaboration for stroke and chest-pain centers, pathology digitization and surgical planning for liver, thoracic and congenital heart workflows.
The same platform logic supports research and teaching through high-quality multimodal specialty knowledge bases, AR-enabled surgical teaching and workflow orchestration that converts evidence-based medical logic into executable reasoning paths.
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