Generative AI In Healthcare Certification

•What is Generative AI? Concepts and principles explained in a healthcare context

•Key differences between traditional AI and generative AI

•Application in Personalized medicine

•Application using Predictive analytics for disease prevention

•Application in Drug discovery and development

•Application in Virtual patient simulations

•High-level overview of deep generative models (VAEs, GANs, diffusion models)

•Focus on what they do rather than coding how they work

•Use Case: GANs in medical imaging (denoising, image synthesis)

•Use Case: VAEs for data augmentation in small datasets

•Diffusion models for medical research (brief overview)

•Use Case: Match the right model type to clinical use cases

•Personalized treatment recommendations (examples from oncology, pharmacology)

•Disease prediction and prevention with AI-enhanced analytics

•Medical imaging analysis and augmentation

•Drug discovery and molecule design with AI

•Case study: Early detection of diseases using AI-generated data

•Case study: Pharma applications in drug pipeline acceleration

•Exercise: Interpreting AI outputs for a diagnostic support scenario

•Why medical image analysis matters in clinical settings

•AI’s role in improving diagnostic accuracy and workflow efficiency

•How synthetic medical images are generated and applied

•Use Cases: Radiology, pathology, CT/MRI/X-ray support

•Privacy concerns in healthcare data (anonymization, encryption, HIPAA/GDPR basics)

•Ethical issues: bias, fairness, transparency, and accountability

•Risks of incorrect or misleading AI-generated outputs

•Case Study: Consequences of biased AI in patient outcomes

•Use Case: Spotting ethical red flags in AI use cases

•Why validation and clinical trials are critical for AI adoption

•Evaluating safety, accuracy, and generalizability of AI solutions

•Integrating AI into hospital/clinic workflows

•Red flags: situations where AI could mislead clinicians

•Transfer learning for healthcare: reusing existing AI to save time and resources

•Interpretability and explainability in clinical AI

•Use Case: Using simple explainability tools (e.g., SHAP visuals) to interpret AI model outputs

•Brief introduction to adversarial risks (kept high-level, no coding)

•Practical challenges of deploying AI in hospitals and clinics

•Scalability, reliability, and integration with electronic health records

•Regulatory requirements (FDA approval process, CE marking, HIPAA/GDPR compliance)

•Continuous monitoring, retraining, and safe use in clinical environments

•Case Study: Example of a hospital deploying AI for imaging support

•Introduction to prompt engineering (explained simply)

•How prompts improve AI-generated responses in clinical contexts

•Practical Applications in summarizing clinical notes

•Generating patient-friendly explanations using Generative AI

•How Generative AI in supporting virtual consultations

•Use Case: Writing effective prompts for medical chatbots or documentation support

•Hands-On : Building better AI queries to get safer, more reliable medical outputs

•Personalized 1-on-1 Trainer/SME Connect Session - Receive a 1-on-1 connect session from Trainer/SME to resolve all types of queries.

•Daily Live Session with Lifelong Learning - Participate in a daily live session with industry experts where professionals from around the world connect with SMEs and have a brainstorming session.