Course Objective & Scope
Vision AI in hospitals extends well beyond radiology: preventing inpatient falls on wards, monitoring bed exits in ICUs, tracking operating-room turnover and counting surgical sponges and instruments directly affect patient safety and capacity. This course focuses on computer vision systems built on in-hospital cameras and depth or thermal sensors.
Privacy-by-design processing on edge devices without storing raw video, real-time face blurring, skeleton-based fall-risk analysis and integration with HIS and nurse-call systems are covered through practical case studies. Participants design a privacy-preserving clinical operations pilot tailored to their healthcare organisation.
Who Should Attend?
- AI and software engineers at hospital groups and healthtech companies
- Biomedical, clinical engineering and hospital information system (HIS) specialists
- Technical leads managing healthcare quality, patient safety and digital transformation projects
- Prerequisite: Basic Python and familiarity with core computer vision concepts
Key Takeaways
- Generate early warnings for patient fall risk from bedside motion and pose analysis
- Select thermal, depth (ToF/LiDAR) and RGB sensors according to privacy constraints
- Automatically monitor operating-room turnover, hand hygiene and sterile-field compliance
- Build real-time face/identity redaction and zero-storage edge architectures on camera feeds
- Design thresholding and nurse-call integrations that minimise clinical alarm fatigue
Curriculum
Visual AI Scenarios in Hospital Operations
- Operational bottlenecks across wards, ICUs, emergency departments and operating rooms
- Suitability of RGB, infrared (IR), thermal and depth (ToF) sensors in clinical rooms
Patient Safety: Fall Prevention and In-Bed Monitoring
- Bed-exit early warning via pose estimation and bed-boundary detection
- Repositioning tracking for pressure-ulcer prevention and agitation analysis
Operating Room and Infection-Control Applications
- Surgical instrument/sponge counting and tracking OR occupancy and turnover times
- Vision-based verification of hand-hygiene stations and PPE (mask, gown, cap) compliance
Patient Privacy, Data Protection and Edge Anonymisation
- In-memory inference without disk recording, transmitting only metadata (skeletons/events)
- Real-time face/identity blurring and alignment with ethics board and privacy regulations
HIS Integration and Managing Alarm Fatigue
- Event integration with HL7/FHIR, nurse-call systems and clinical dashboards
- Reducing false positives, temporal smoothing and clinical validation protocols
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