Edge AI for Healthcare Training is a focused two-day program designed to help healthcare and technology professionals understand how deploying AI at the edge enables real-time, secure, and reliable healthcare applications. This course covers Edge AI concepts, healthcare-specific architectures, on-device intelligence, and clinical use cases such as medical imaging, patient monitoring, diagnostics support, and privacy-preserving AI, enabling participants to assess and apply Edge AI in modern healthcare environments.

• Understand Edge AI in healthcare
• Learn real-time healthcare data processing
• Understand AI-enabled patient monitoring
• Gain knowledge of medical edge devices
• Learn healthcare automation concepts
• Understand low-latency healthcare systems
• Explore AI-assisted diagnostics basics
• Identify healthcare Edge AI use cases

• Healthcare professionals and clinicians
• Digital health and health informatics teams
• AI and data science professionals in healthcare
• Medical device and health technology engineers
• Healthcare innovation and transformation leaders

• Basic understanding of healthcare workflows or clinical environments
• Familiarity with medical data or digital health systems is beneficial
• General awareness of artificial intelligence concepts
• Interest in real-time and privacy-focused healthcare AI

• Understanding Edge AI concepts in healthcare contexts
• Awareness of deploying AI models on medical and edge devices
• Knowledge of real-time healthcare AI use cases
• Evaluating data privacy, safety, and regulatory considerations
• Interpreting real-world Edge AI healthcare applications

By the end of this training, participants will be able to explain how Edge AI is applied in healthcare, understand deployment and optimization of AI models on edge medical devices, evaluate privacy and regulatory requirements, and assess how Edge AI improves real-time decision-making and patient care.

Introduction to Edge AI in Healthcare
• Definition and scope of Edge AI for healthcare
• Difference between cloud-based and edge-based healthcare AI
• Benefits of low latency, privacy, and on-device intelligence
• Overview of Edge AI healthcare use cases

Healthcare Edge Architecture and Devices
• Medical devices, sensors, and edge computing platforms
• Wearables and remote patient monitoring systems
• Data flow between edge, hospital systems, and cloud
• Hardware and infrastructure considerations

AI Models for Healthcare Edge Deployment
• Selecting models suitable for healthcare edge environments
• Accuracy, latency, and reliability trade-offs
• Lightweight models for imaging and signal analysis
• Evaluating model performance in clinical settings

Model Optimization and Deployment
• Model compression and efficiency concepts
• Deployment pipelines for healthcare edge AI
• Updating and maintaining models on medical devices
• Monitoring performance and reliability

Privacy, Security, and Regulatory Compliance
• Patient data privacy and on-device processing
• Security considerations for medical AI systems
• Regulatory and compliance awareness
• Risk management and ethical considerations

Healthcare Use Cases and Future Trends
• Medical imaging and diagnostics support
• Continuous patient monitoring and alerts
• Smart medical devices and hospital operations
• Future directions of Edge AI in healthcare

• Edge AI fundamentals
• Healthcare AI concepts
• Patient monitoring systems
• Medical edge devices
• Real-time healthcare processing
• AI-assisted diagnostics
• Healthcare IoT integration
• Data privacy and security basics
• Compliance and ethical considerations
• Practical healthcare AI scenarios

• Conceptual understanding assessments
• Healthcare-focused use case analysis exercises
• Privacy and safety evaluation activity
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Edge AI for Healthcare Training, validating their expertise in deploying and evaluating Edge AI solutions for real-time, secure, and compliant healthcare applications.