TinyML in Healthcare: AI on Wearable Devices Training Course

This instructor-led, live training in New Zealand (available online or onsite) is designed for intermediate to advanced healthcare professionals and AI developers who wish 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 New Zealand (online or on-site) is designed for intermediate-level healthcare professionals who wish to apply AI and AR/VR solutions in medical training, surgical simulations, and rehabilitation.

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

• Understand the role of AI in enhancing AR/VR experiences within healthcare.
• Use AR/VR for surgical simulations and medical training.
• Apply AR/VR tools in patient rehabilitation and therapy.
• Explore ethical and privacy concerns related to AI-enhanced medical tools.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level data scientists and healthcare professionals who wish 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.
• Use AI for predictive modelling in healthcare data.
• Analyse medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

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

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

• Identify key healthcare challenges that AI can address.
• Analyse 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.

This instructor-led training in New Zealand empowers advanced professionals with the skills to design, optimise, and deploy complete TinyML pipelines. Participants will learn to collect data, train low-power models, and test real-world applications through practical laboratories.

This instructor-led, live training in New Zealand (available online or on-site) is designed for healthcare professionals and researchers who wish to harness ChatGPT to improve patient care, streamline workflows, and enhance healthcare outcomes.

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

• Understand the fundamentals of ChatGPT and its applications in healthcare.
• Leverage ChatGPT to automate healthcare processes and interactions.
• Provide accurate medical information and support to patients using ChatGPT.
• Apply ChatGPT for medical research and analysis.

This instructor-led, live training in New Zealand (delivered online or on-site) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to harness Edge AI to create innovative healthcare solutions.

By the conclusion 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 in New Zealand (online or on-site) is designed for intermediate to advanced-level medical AI developers and data scientists who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using both structured and unstructured medical data.

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

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

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for healthcare professionals, data analysts, and policy makers at beginner to intermediate levels who wish to understand and apply generative AI within the healthcare context.

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

• Explain the core principles and practical applications of generative AI in healthcare.
• Identify opportunities where generative AI can enhance drug discovery and support personalised medicine.
• Apply generative AI techniques to medical imaging and diagnostic processes.
• Evaluate the ethical implications of deploying AI in clinical and medical settings.
• Develop effective strategies for integrating AI technologies into existing healthcare systems.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate to advanced-level healthcare professionals, medical researchers, and AI developers who wish to apply multimodal AI in medical diagnostics and healthcare applications.

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

• Understand the role of multimodal AI in modern healthcare.
• Integrate structured and unstructured medical data for AI-driven diagnostics.
• Apply AI techniques to analyse 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.

This live training in New Zealand enables healthcare professionals and IT teams to deploy Ollama for local LLMs. It covers integrating AI into clinical workflows, customising models for specific duties, and ensuring robust data privacy and regulatory compliance within secure healthcare settings.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level healthcare professionals and AI developers who wish to apply prompt engineering techniques to improve medical workflows, research efficiency, and patient outcomes.

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

• Grasp the fundamentals of prompt engineering within the healthcare sector.
• Utilise AI prompts for clinical documentation and patient interactions.
• Leverage AI to support medical research and literature reviews.
• Enhance drug discovery and clinical decision-making through AI-driven prompts.
• Ensure compliance with regulatory and ethical standards in healthcare AI.