Generative AI and Prompt Engineering in Healthcare Training Course

Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation through its built-in libraries and services.

This instructor-led live training (available online or onsite) targets intermediate to advanced practitioners seeking to improve the performance and reliability of generative AI applications by utilizing supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

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

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement prompt management workflows, including version control and testing.
• Utilize evaluation libraries to benchmark and optimize AI performance.
• Deploy and monitor improved models in production environments.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on Vertex AI fine-tuning and prompt tools.
• Case studies highlighting enterprise model optimization.

Course Customization Options

• To request a customized version of this course, please contact us to arrange.

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

This guided, live training session (available online or in-person) is designed for intermediate-level healthcare and data professionals looking to design, assess, and manage agentic AI solutions for both clinical and operational scenarios.

Upon completing this training, participants will be able to:

• Articulate the core concepts and limitations of agentic AI within healthcare environments.
• Create secure agent workflows incorporating planning, memory capabilities, and tool integration.
• Develop retrieval-augmented agents that leverage clinical documents and knowledge bases.
• Assess, monitor, and govern agent behavior using safety guardrails and human-in-the-loop controls.

Course Format

• Interactive lectures combined with facilitated group discussions.
• Guided laboratory exercises and code walkthroughs conducted in a sandbox environment.
• Scenario-based activities focusing on safety protocols, evaluation methods, and governance frameworks.

Customization Options

• To request a tailored training version of this course, please contact us to make arrangements.

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

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

• Comprehend the role of AI agents in healthcare and diagnostics.
• Build 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 adhere to ethical AI practices.

This instructor-led, live training session in Slovakia (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.

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

• Comprehending how AI improves AR/VR experiences within healthcare.
• Utilizing AR/VR for surgical simulations and medical training.
• Implementing AR/VR tools for patient rehabilitation and therapy.
• Examining the ethical and privacy issues related to AI-enhanced medical instruments.

This instructor-led live training in Slovakia (accessible online or onsite) is designed for intermediate data scientists and healthcare professionals seeking to harness AI for advanced medical applications using Google Colab.

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

• Deploy AI models for healthcare applications using Google Colab.
• Utilize AI for predictive modeling in healthcare datasets.
• Perform medical image analysis using AI-driven techniques.
• Investigate ethical considerations in AI-based healthcare solutions.

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

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

• Grasp the fundamentals of ChatGPT and its applications in healthcare.
• Employ ChatGPT to automate healthcare processes and interactions.
• Deliver accurate medical information and support to patients via ChatGPT.
• Apply ChatGPT for medical research and analysis.

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

Upon completing this training, participants will be capable of:

• Grasping the advantages and role of Edge AI in healthcare.
• Creating and deploying AI models on edge devices for healthcare use cases.
• Integrating Edge AI solutions into diagnostic tools and wearable devices.
• Designing and deploying patient monitoring systems powered by Edge AI.
• Navigating regulatory and ethical considerations in healthcare AI applications.

This guided, live training held in Slovakia (online or onsite) targets medical AI developers and data scientists with intermediate to advanced skills who intend to adapt models for clinical diagnosis, disease prediction, and forecasting patient outcomes using structured and unstructured medical data.

By the conclusion 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.

This instructor-led, live training in Slovakia (online or onsite) is designed for healthcare professionals, data analysts, and policymakers at a beginner to intermediate level who wish to understand and apply generative AI within 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 facilitates stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enhancing interoperability, and developing decision-support systems that align with medical workflows.

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

Upon completing 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 using real-world case studies.
• Implementation practice in a live-lab environment.

Customization Options

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

This instructor-led, live training in Slovakia (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 is a lightweight platform designed for running large language models locally.

This instructor-led, live training (available online or onsite) targets intermediate-level healthcare practitioners and IT teams looking 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 Slovakia (online or onsite) is designed for intermediate-level healthcare professionals and AI developers who want to apply prompt engineering techniques to improve medical workflows, research efficiency, and patient outcomes.

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

• Grasp the fundamentals of prompt engineering within the healthcare context.
• Utilize 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.

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

This instructor-led live training (available online or onsite) is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostics.

Upon completion of 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 derive AI-driven insights.
• Optimize models for low-power and memory-limited wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-generated outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Hands-on practice utilizing wearable device data and TinyML frameworks.
• Guided lab exercises for implementation.

Customization Options

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