AI in Healthcare Training Course

Agentic AI represents a paradigm where AI systems independently plan, reason, and utilize tools to achieve objectives within specific boundaries.

This instructor-led live training, available both online and onsite, is tailored for intermediate-level healthcare and data teams looking to design, evaluate, and govern agentic AI solutions for clinical and operational scenarios.

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

• Articulate the core concepts and constraints of agentic AI within healthcare environments.
• Design secure agent workflows that incorporate planning, memory, and tool integration.
• Develop retrieval-augmented agents utilizing clinical documents and knowledge bases.
• Evaluate, monitor, and govern agent behavior through the use of 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

• For those interested in a customized version of this course, please reach out to us to make arrangements.

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

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

• Grasp the significance of AI agents in healthcare and diagnostic processes.
• Create AI models for medical image analysis and predictive diagnostics.
• Integrate AI systems with electronic health records (EHR) and clinical workflows.
• Adhere to healthcare regulations and ethical AI standards.

This instructor-led, live training in Sweden (online or onsite) is aimed at intermediate-level healthcare professionals who wish to apply AI and AR/VR solutions for medical training, surgery simulations, and rehabilitation.

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

• Understand the role of AI in enhancing 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 instructor-led, live training in Sweden (online or on-site) 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 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 Sweden (online or onsite) is designed for healthcare professionals and researchers who wish to leverage ChatGPT to enhance patient care, streamline workflows, and improve healthcare outcomes.

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

• Understand the fundamentals of ChatGPT and its applications in healthcare.
• Utilize 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 Sweden (online or onsite) targets intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.

Upon completing this training, participants will be able to:

• Comprehend the role and advantages of Edge AI in healthcare.
• Create and deploy AI models on edge devices for healthcare applications.
• Integrate Edge AI solutions into wearable devices and diagnostic tools.
• Design and implement patient monitoring systems using Edge AI.
• Address ethical and regulatory aspects in healthcare AI applications.

This instructor-led, live training in Sweden (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 Sweden (online or onsite) is designed for beginner to intermediate-level healthcare professionals, data analysts, and policy makers who wish to comprehend and implement generative AI solutions in the context of healthcare.

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

• Describe the core principles and practical applications of generative AI in healthcare.
• Identify opportunities where generative AI can improve drug discovery and personalized medicine.
• Apply generative AI techniques to medical imaging and diagnostic processes.
• Evaluate the ethical implications of using AI in medical environments.
• Formulate 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 essential for ensuring compliance, enabling interoperability, and developing decision-support systems that seamlessly integrate with medical workflows.

This instructor-led live training, available online or onsite, is designed for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while effectively addressing regulatory, ethical, and operational challenges.

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

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

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Sweden (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.

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 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 provides a lightweight platform for executing large language models directly on local hardware.

This guided, live training—available either online or on-site—is designed for intermediate healthcare professionals and IT teams looking to deploy, tailor, and manage AI solutions based on Ollama within clinical and administrative contexts.

After finishing this training, participants will be equipped to:

• Install and set up Ollama to ensure secure usage in healthcare environments.
• Embed local large language models into both clinical workflows and administrative tasks.
• Tailor models to handle healthcare-specific terminology and responsibilities.
• Implement best practices for privacy, security, and adherence to regulatory standards.

Course Format

• Interactive lectures and discussions.
• Practical demonstrations and guided exercises.
• Hands-on application in a sandboxed healthcare simulation environment.

Customization Options

• To arrange a customized version of this training, please get in touch with us.

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

Upon completing this training, participants will be able to:

• Grasp the core principles of prompt engineering within the healthcare context.
• Utilize AI prompts for clinical documentation and patient communication.
• Harness AI capabilities for medical research and literature reviews.
• Improve drug discovery and clinical decision-making through AI-driven prompts.
• Adhere to regulatory and ethical standards in healthcare AI applications.

TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training (available online or onsite) targets intermediate-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be able to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimize models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

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

Customization Options

• For tailored training aligned with specific healthcare devices or regulatory workflows, please contact us to customize the program.