TinyML in Healthcare: AI on Wearable Devices Training Course

Agentic AI refers to a methodology where AI systems autonomously plan, reason, and utilize tools to achieve specific objectives within established constraints.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare and data teams seeking to design, evaluate, and govern agentic AI solutions for clinical and operational applications.

Upon completing this training, participants will be capable of:

• Articulating agentic AI concepts and constraints within healthcare contexts.
• Designing secure agent workflows incorporating planning, memory, and tool utilization.
• Developing retrieval-augmented agents that process clinical documents and knowledge bases.
• Evaluating, monitoring, and governing agent behavior through guardrails and human-in-the-loop controls.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based exercises focusing on safety, evaluation, and governance.

Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in Taiwan (online or onsite) is designed for intermediate to advanced healthcare professionals and AI developers looking to implement AI-driven healthcare solutions.

Upon completion of this training, participants will be able to:

• Grasp the role of AI agents in healthcare and diagnostics.
• Develop AI models for medical image analysis and predictive diagnostics.
• Integrate AI with electronic health records (EHR) and clinical workflows.
• Ensure compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training in Taiwan (online or onsite) is designed for intermediate-level healthcare professionals looking to implement AI and AR/VR solutions for medical training, surgical simulations, and rehabilitation.

Upon completion of this training, participants will be able to:

• Grasp how AI improves AR/VR experiences in healthcare.
• Utilize AR/VR for surgical simulations and medical education.
• Deploy AR/VR tools in patient rehabilitation and therapeutic settings.
• Examine the ethical and privacy issues associated with AI-enhanced medical instruments.

This instructor-led live training in Taiwan (available online or onsite) is designed for intermediate-level data scientists and healthcare professionals seeking to leverage AI for advanced healthcare applications using Google Colab.

By the end of this training, participants will be able to:

• Implement AI models for healthcare using Google Colab.
• Apply AI for predictive modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in Taiwan (online or onsite) is designed for intermediate-level healthcare professionals and data scientists who wish to understand and apply AI technologies in healthcare environments.

By the end of this training, participants will be able to:

• Identify key healthcare challenges that AI can address.
• Analyze AI’s impact on patient care, safety, and medical research.
• Understand the relationship between AI and healthcare business models.
• Apply fundamental AI concepts to healthcare scenarios.
• Develop machine learning models for medical data analysis.

TinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available online or on-site, is designed for advanced technical professionals who aim to design, optimize, and deploy complete TinyML pipelines.

Upon completing this training, participants will gain the ability to:

• Collect, prepare, and manage datasets specifically for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware environments.

Course Format

• Instructor-guided lectures and technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Course Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in Taiwan (online or onsite) is designed for healthcare professionals and researchers who aim to leverage ChatGPT to enhance patient care, streamline workflows, and improve healthcare outcomes.

Upon completing this training, participants will be able to:

• Grasp the core principles of ChatGPT and its applications in healthcare.
• Deploy ChatGPT to automate healthcare procedures and interactions.
• Deliver precise medical information and support to patients via ChatGPT.
• Implement ChatGPT in medical research and analysis.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.

By the end of this training, participants will be able to:

• Understand the role and benefits of Edge AI in healthcare.
• Develop and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Address ethical and regulatory considerations in healthcare AI applications.

This instructor-led live training, available online or onsite, is designed for intermediate to advanced medical AI developers and data scientists who aim to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

By the end of this training, participants will be able to:

• Fine-tune AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression in medical contexts.
• Address privacy, bias, and regulatory compliance in model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

Generative AI is a technology that creates new content such as text, images, and recommendations based on prompts and data.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals who wish to use generative AI and prompt engineering to improve efficiency, accuracy, and communication in medical contexts.

By the end of this training, participants will be able to:

• Understand the fundamentals of generative AI and prompt engineering.
• Apply AI tools to streamline clinical, administrative, and research tasks.
• Ensure ethical, safe, and compliant use of AI in healthcare.
• Optimize prompts to achieve consistent and accurate results.

Format of the Course

• Interactive lecture and discussion.
• Practical exercises and case studies.
• Hands-on experimentation with AI tools.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Taiwan (online or onsite) is designed for beginner to intermediate-level healthcare professionals, data analysts, and policymakers who wish to understand and apply generative AI within the healthcare context.

Upon completion of this training, participants will be able to:

• Explain the principles and applications of generative AI in healthcare.
• Identify opportunities for generative AI to enhance drug discovery and personalized medicine.
• Utilize generative AI techniques for medical imaging and diagnostics.
• Assess the ethical implications of AI in medical settings.
• Develop strategies for integrating AI technologies into healthcare systems.

LangGraph facilitates stateful, multi-agent workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are vital for ensuring compliance, interoperability, and the development of decision-support systems that align with clinical workflows.

This instructor-led live training, available online or onsite, targets intermediate to advanced professionals looking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be able to:

• Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
• Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications in healthcare production settings.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises utilizing real-world case studies.
• Implementation practice within a live laboratory environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange it.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals, medical researchers, and AI developers who wish to apply multimodal AI in medical diagnostics and healthcare applications.

Upon completion of this training, participants will be able to:

• Grasp the role of multimodal AI in modern healthcare.
• Integrate structured and unstructured medical data for AI-driven diagnostics.
• Apply AI techniques to analyze medical images and electronic health records.
• Develop predictive models for disease diagnosis and treatment recommendations.
• Implement speech and natural language processing (NLP) for medical transcription and patient interaction.

Ollama is a lightweight platform for running large language models locally.

This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare practitioners and IT teams who wish to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare settings.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models for healthcare-specific terminology and tasks.
• Apply best practices for privacy, security, and regulatory compliance.

Format of the Course

• Interactive lecture and discussion.
• Hands-on demonstrations and guided exercises.
• Practical implementation in a sandboxed healthcare simulation environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level healthcare professionals and AI developers who wish to leverage prompt engineering techniques for improving medical workflows, research efficiency, and patient outcomes.

By the end of this training, participants will be able to:

• Understand the fundamentals of prompt engineering in healthcare.
• 運用 AI 提示進行臨床文書撰寫及患者互動。
• Leverage AI for medical research and literature review.
• Enhance drug discovery and clinical decision-making with AI-driven prompts.
• Ensure compliance with regulatory and ethical standards in healthcare AI.