AI for Healthcare Training in Patras

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

Upon completion 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 techniques within medical contexts.
• Address privacy concerns, bias, and regulatory compliance during 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 Patras (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 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 Patras (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 Patras (online or onsite) is designed for healthcare professionals, data analysts, and policymakers at beginner to intermediate levels 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.

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 Patras (online or on-site) is designed for 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:

• 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 Patras (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.

Agentic AI represents a methodology wherein AI systems engage in planning, reasoning, and utilizing tools to achieve objectives within established boundaries.

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

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

• Explaining the core concepts and constraints of agentic AI within healthcare environments.
• Designing secure agent workflows incorporating planning, memory, and tool integration.
• Developing retrieval-augmented agents that operate over clinical documents and knowledge repositories.
• Evaluating, monitoring, and governing agent behavior through the implementation of guardrails and human-in-the-loop controls.

Course Format

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

Customization Options

• For information regarding customized training for this course, please reach out to us to arrange.

This instructor-led live training in Patras (online or onsite) targets intermediate to advanced healthcare professionals, medical researchers, and AI developers wishing 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 contemporary healthcare.
• Combine structured and unstructured medical data for AI-driven diagnostics.
• Utilize AI techniques to analyse medical images and electronic health records.
• Create predictive models for disease diagnosis and treatment recommendations.
• Implement speech recognition and natural language processing (NLP) for medical transcription and patient engagement.

This instructor-led, live training Patras (online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers aiming to leverage Edge AI for innovative healthcare solutions.

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

• Understand the role and advantages 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.

LangGraph facilitates 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, enhancing interoperability, and developing decision-support systems that integrate seamlessly with medical workflows.

This instructor-led live training, available both online and onsite, targets intermediate to advanced-level professionals aiming 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 a focus on compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications in healthcare production settings.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with 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 Patras (online or onsite) targets intermediate-level healthcare professionals and AI developers who wish to apply prompt engineering techniques to enhance 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 refers to the integration of machine learning algorithms into low-power, resource-constrained wearable and medical devices.

This instructor-led, live training session (available online or onsite) is designed for intermediate-level practitioners who aim to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be equipped to:

• Design and deploy TinyML models for the real-time processing of health data.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimize models for wearables with limited power and memory capacity.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures augmented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Implementation tasks within a guided laboratory environment.

Customization Options for the Course

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

This instructor-led, live training in Patras (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.