TinyML in Healthcare Training is a focused two-day program designed to help healthcare and technology professionals understand how machine learning can be deployed directly on low-power medical and healthcare devices. This course explores how TinyML enables on-device intelligence for real-time monitoring, diagnostics support, and privacy-preserving healthcare applications, allowing AI to operate reliably on wearable devices, sensors, and embedded medical systems without continuous cloud connectivity.

• Understand TinyML in healthcare
• Learn AI on medical edge devices
• Understand real-time patient monitoring concepts
• Gain knowledge of healthcare sensor integration
• Learn low-power healthcare AI workflows
• Understand embedded AI deployment basics
• Explore AI-assisted healthcare applications
• Identify TinyML healthcare use cases

• Healthcare technology and biomedical engineers
• Digital health and health IoT professionals
• Medical device developers and product teams
• Healthcare innovation and R&D teams
• Clinicians and technologists involved in smart healthcare solutions

• Basic understanding of healthcare workflows or medical device environments
• Familiarity with sensors, wearables, or healthcare IoT concepts
• Awareness of artificial intelligence or machine learning fundamentals
• No advanced programming or embedded systems background required

• Understanding TinyML concepts in healthcare contexts
• Identifying healthcare use cases suitable for TinyML
• Interpreting on-device ML outputs responsibly
• Evaluating constraints of power, latency, and reliability
• Applying ethical and privacy-aware TinyML practices in healthcare

By the end of this training, participants will be able to understand how TinyML enables on-device intelligence in healthcare, identify suitable clinical and medical device use cases, evaluate deployment constraints, interpret results responsibly, and contribute to secure, ethical, and efficient TinyML-based healthcare solutions.

Introduction to TinyML in Healthcare
• What TinyML is and why it matters in healthcare
• Difference between cloud AI, edge AI, and TinyML
• Benefits of on-device intelligence for healthcare
• Overview of TinyML-enabled healthcare systems

Healthcare Data and Sensor Intelligence
• Physiological signals and healthcare sensor data
• Wearables, implantables, and medical IoT devices
• Data quality, noise, and reliability considerations
• Real-time data processing on constrained devices

TinyML Healthcare Use Cases
• Continuous patient monitoring and alerts
• Early anomaly detection in vital signs
• Assistive diagnostics and decision support
• Smart medical devices and home healthcare

Model Optimization for Healthcare TinyML
• Memory, power, and latency constraints
• Model compression and quantization concepts
• Balancing accuracy and energy efficiency
• Evaluating TinyML performance in healthcare scenarios

Deployment and Operational Challenges
• Integrating TinyML models into medical devices
• Reliability and fault tolerance in healthcare environments
• Device lifecycle management and updates
• Supporting clinical trust and usability

Ethics, Privacy, and Responsible TinyML in Healthcare
• Patient data privacy and on-device processing benefits
• Bias, safety, and risk considerations
• Regulatory awareness for healthcare devices
• Responsible and trustworthy TinyML adoption

• TinyML fundamentals
• Healthcare edge AI concepts
• Patient monitoring systems
• Medical sensor integration
• Embedded AI workflows
• Real-time healthcare analytics
• Low-power AI processing
• Data privacy and compliance basics
• Healthcare AI security considerations
• Practical TinyML healthcare scenarios

• Healthcare TinyML use case identification exercise
• Deployment constraint and optimization discussion
• Responsible AI and privacy scenario analysis
• 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 TinyML in Healthcare Training, validating their expertise in applying TinyML concepts to healthcare use cases, device constraints, and responsible on-device AI deployment.