Format: Live instructor-led online training via Zoom / Microsoft Teams

Advanced Generative AI in Healthcare Training Course

Generative Artificial Intelligence (Generative AI) is transforming healthcare by revolutionizing clinical decision support, medical documentation, healthcare research, drug discovery, personalized medicine, patient engagement, and operational efficiency. The Advanced Generative AI in Healthcare Training Course equips healthcare professionals, researchers, health informatics specialists, AI engineers, policymakers, and healthcare leaders with advanced knowledge and practical skills to design, implement, govern, and optimize Generative AI solutions across healthcare ecosystems. The course explores cutting-edge technologies including Large Language Models (LLMs), Foundation Models, Retrieval-Augmented Generation (RAG), Multimodal AI, Clinical Decision Support Systems (CDSS), Natural Language Processing (NLP), Medical Imaging AI, Healthcare Chatbots, AI Copilots, Intelligent Clinical Documentation, Precision Medicine, Predictive Analytics, Electronic Health Records (EHR), Explainable AI (XAI), Healthcare Data Science, AI Governance, Responsible AI, and Digital Health Transformation, enabling participants to drive innovation using secure and ethical AI solutions.

Participants will gain practical experience in applying Generative AI to automate clinical documentation, summarize patient encounters, generate discharge summaries, assist clinical coding, support medical research, accelerate literature reviews, improve diagnostic workflows, enhance patient communication, develop virtual healthcare assistants, and create AI-powered knowledge management systems. Through hands-on exercises using modern AI platforms, prompt engineering techniques, Retrieval-Augmented Generation architectures, vector databases, healthcare knowledge graphs, multimodal foundation models, and secure AI deployment frameworks, participants will build intelligent healthcare solutions that improve efficiency while maintaining clinical accuracy, patient safety, and regulatory compliance.

Healthcare organizations worldwide are increasingly investing in Generative AI to reduce administrative burden, improve healthcare quality, accelerate research, optimize operational performance, and strengthen evidence-based medicine. This course provides comprehensive guidance on AI governance, healthcare cybersecurity, privacy-preserving AI, ethical implementation, regulatory compliance, healthcare interoperability, model evaluation, hallucination mitigation, human-in-the-loop systems, and responsible deployment of AI technologies. Participants will learn how to integrate Generative AI into Electronic Health Records (EHR), Hospital Information Systems (HIS), Laboratory Information Management Systems (LIMS), Radiology Information Systems (RIS), telemedicine platforms, and enterprise healthcare workflows while ensuring transparency, accountability, and trust.

The training combines expert-led lectures, practical laboratories, collaborative workshops, healthcare simulations, AI application development, prompt engineering projects, and comprehensive case studies from hospitals, ministries of health, pharmaceutical companies, research institutions, insurance providers, humanitarian organizations, academic medical centers, and digital health startups. Upon successful completion, participants will possess the strategic and technical capabilities to lead enterprise Generative AI initiatives, implement intelligent healthcare automation, improve clinical decision-making, enhance patient-centered care, accelerate medical innovation, and support sustainable digital transformation aligned with international healthcare standards and responsible AI best practices.

Course Objectives

1. Understand Generative AI concepts and healthcare applications.
2. Apply Large Language Models (LLMs) in clinical decision support.
3. Design AI-powered healthcare assistants and intelligent workflows.
4. Automate clinical documentation using Generative AI.
5. Develop Retrieval-Augmented Generation (RAG) healthcare solutions.
6. Integrate Generative AI with Electronic Health Records (EHR).
7. Implement responsible AI governance and regulatory compliance.
8. Evaluate AI model performance, safety, and explainability.
9. Strengthen healthcare research and innovation using Generative AI.
10. Develop enterprise strategies for Generative AI implementation in healthcare.

Organizational Benefits

1. Reduce administrative workload through intelligent automation.
2. Improve clinical documentation accuracy and efficiency.
3. Enhance evidence-based clinical decision-making.
4. Accelerate healthcare research and knowledge discovery.
5. Improve patient engagement through AI-powered communication.
6. Optimize hospital workflows and operational performance.
7. Strengthen healthcare innovation and digital transformation.
8. Improve regulatory compliance and responsible AI governance.
9. Enhance healthcare data utilization and organizational intelligence.
10. Build institutional capacity for secure and scalable AI adoption.

Target Participants

• Medical Doctors
• Nurses
• Clinical Officers
• Hospital Administrators
• Health Informatics Specialists
• Health Information Managers
• Healthcare Data Scientists
• Artificial Intelligence Engineers
• Machine Learning Engineers
• Data Analysts
• Clinical Researchers
• Biomedical Engineers
• Medical Laboratory Scientists
• Pharmacists
• Radiologists
• Public Health Specialists
• Digital Health Specialists
• Healthcare IT Professionals
• Policy Makers
• Healthcare Consultants
• Pharmaceutical Researchers
• NGO Health Program Managers
• University Researchers
• Innovation Managers
• Telemedicine Professionals

Course Outline

Module 1: Introduction to Generative AI in Healthcare

• Fundamentals of Generative AI
• Evolution of healthcare AI
• Large Language Models (LLMs)
• Foundation models
• Healthcare AI ecosystem
• Case Study: Enterprise adoption of Generative AI in a hospital

Module 2: Prompt Engineering for Healthcare Applications

• Prompt design principles
• Clinical prompt optimization
• Role-based prompting
• Chain-of-thought strategies
• Prompt evaluation techniques
• Case Study: Clinical documentation using prompt engineering

Module 3: Large Language Models for Clinical Decision Support

• Clinical reasoning support
• Medical knowledge generation
• AI-assisted diagnosis
• Differential diagnosis support
• Treatment planning assistance
• Case Study: AI-assisted clinical consultation workflow

Module 4: Retrieval-Augmented Generation (RAG)

• RAG architecture
• Vector databases
• Healthcare knowledge retrieval
• Medical document indexing
• Context-aware AI responses
• Case Study: Hospital knowledge management using RAG

Module 5: Intelligent Clinical Documentation

• Automated medical transcription
• SOAP note generation
• Discharge summary generation
• Clinical coding assistance
• Medical report automation
• Case Study: AI-powered electronic medical documentation

Module 6: Generative AI for Medical Research

• Literature review automation
• Scientific writing support
• Research data summarization
• Clinical trial analysis
• Knowledge synthesis
• Case Study: Accelerating medical research using Generative AI

Module 7: Multimodal AI in Healthcare

• Text-to-image applications
• Image interpretation support
• Medical imaging foundation models
• Speech recognition
• Voice-enabled healthcare assistants
• Case Study: Multimodal AI for radiology reporting

Module 8: AI-Powered Patient Engagement

• Virtual healthcare assistants
• Conversational AI
• Patient education automation
• Personalized healthcare communication
• Remote patient support
• Case Study: AI chatbot for chronic disease management

Module 9: Enterprise Integration of Generative AI

• Electronic Health Record integration
• Hospital Information Systems integration
• API development
• Workflow automation
• Cloud AI deployment
• Case Study: Enterprise AI integration across hospital systems

Module 10: AI Governance, Ethics and Regulatory Compliance

• Responsible AI principles
• Healthcare data privacy
• AI risk management
• Explainable AI
• Regulatory compliance frameworks
• Case Study: Ethical governance of Generative AI in healthcare

Module 11: AI Security, Evaluation and Performance Optimization

• Hallucination detection
• Model validation
• Human-in-the-loop systems
• AI quality assurance
• Performance monitoring
• Case Study: Validating clinical AI before deployment

Module 12: Future Trends in Generative AI for Healthcare

• AI agents in healthcare
• Autonomous clinical workflows
• Personalized precision medicine
• Digital twins in healthcare
• Emerging AI innovations
• Case Study: Intelligent AI-powered smart hospital ecosystem

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call +254712260031.
14. Website: Visit www.fdc-k.org for more information.