Multimodal AI for Healthcare Training Course

Agentic AI refers to a methodology where AI systems independently plan, reason, and utilize tools to achieve specific objectives within set boundaries.

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

Upon completing this training, participants will be equipped to:

• Articulate agentic AI concepts and constraints within healthcare settings.
• Construct secure agent workflows incorporating planning, memory, and tool integration.
• Develop retrieval-augmented agents that process clinical documents and knowledge bases.
• Assess, monitor, and govern agent behaviour using guardrails and human-in-the-loop controls.

Course Format

• Interactive lectures complemented by 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 get in touch with us to arrange your session.

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

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

• Understand 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 live, instructor-led training in Nigeria (online or on-site) is designed for intermediate-level healthcare professionals aiming to apply AI and AR/VR solutions for medical training, surgical simulations, and rehabilitation.

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

• Understand how AI enhances AR/VR experiences in healthcare.
• Use AR/VR for surgery simulations and medical training.
• Apply AR/VR tools in patient rehabilitation and therapy.
• Explore the ethical and privacy concerns in AI-enhanced medical tools.

This live, instructor-led training in Nigeria (online or onsite) targets intermediate data scientists and healthcare professionals eager to utilize AI for advanced medical applications via Google Colab.

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

• Deploy AI models for healthcare solutions using Google Colab.
• Apply AI for predictive modeling on healthcare data.
• Analyze medical imagery using AI-driven techniques.
• Examine the ethical implications of AI in healthcare.

This instructor-led, live training in Nigeria (online or onsite) is aimed at 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.

This instructor-led, live training in Nigeria (online or onsite) is designed for healthcare professionals and researchers aiming to harness ChatGPT to elevate patient care, optimize workflows, and enhance healthcare outcomes.

Upon completing this training, participants will be equipped to:

• Grasp the core concepts of ChatGPT and its healthcare applications.
• Employ ChatGPT to automate healthcare processes and patient interactions.
• Deliver precise medical information and support to patients via ChatGPT.
• Apply ChatGPT in medical research and analytical tasks.

This live, instructor-led training in Nigeria (online or in-person) is designed for advanced AI developers, machine learning engineers, and researchers who want to create custom multimodal AI models using open-source frameworks.

Upon completing this training, participants will be able to:

• Grasp the core principles of multimodal learning and data fusion.
• Build multimodal models using DeepSeek, OpenAI, Hugging Face, and PyTorch.
• Optimize and fine-tune models for integrating text, image, and audio inputs.
• Deploy multimodal AI models in practical, real-world scenarios.

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

Upon completing this training, participants will be equipped to:

• Fine-tune AI models on healthcare datasets, including Electronic Medical Records (EMRs), imaging, and time-series data.
• Utilise transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Navigate privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in practical healthcare settings.

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:

• Grasp the core concepts of generative AI and prompt engineering.
• Leverage AI tools to streamline clinical, administrative, and research tasks.
• Uphold ethical, safe, and compliant use of AI in healthcare.
• Refine prompts to achieve consistent and accurate results.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Hands-on experimentation with AI tools.

Course Customization Options

• For bespoke training on this course, please contact us to arrange.

This instructor-led, live training in Nigeria (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals, data analysts, and policy makers who wish to understand and apply generative AI in the context of healthcare.

By the end 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 empowers the creation of stateful, multi-actor 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 seamlessly align with clinical workflows.

This instructor-led live training, available online or on-site, targets intermediate to advanced professionals eager to design, implement, and manage LangGraph-based healthcare solutions. The course addresses the regulatory, ethical, and operational challenges inherent in this field.

Upon completion of this training, participants will be capable of:

• Designing healthcare-specific LangGraph workflows that prioritise compliance and auditability.
• Integrating LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Applying best practices for reliability, traceability, and explainability within sensitive environments.
• Deploying, monitoring, and validating LangGraph applications in healthcare production settings.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises grounded in real-world case studies.
• Practical implementation within a live laboratory environment.

Customisation Options

• To arrange a tailored training session for this course, please get in touch with us.

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 Nigeria (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.
• Use AI prompts for clinical documentation and patient interactions.
• 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.

TinyML involves embedding machine learning capabilities into wearable and medical devices that operate with low power and limited resources.

This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

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

• Design and deploy TinyML models for processing health data in real-time.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models to run efficiently on wearable devices with constrained memory and power.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided implementation exercises within a lab environment.

Customization Options

• For specialized training that aligns with specific healthcare devices or regulatory workflows, please reach out to us to tailor the program.