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Generative AI for Healthcare Professionals
Course
Generative AI for Healthcare Professionals
AI is transforming healthcare—learn to apply generative AI to clinical challenges with ethics and regulations in mind.
Online Live Classes
Beginner Level
Mentor Support
The Generative AI for Healthcare Professionals course provides a comprehensive introduction to the concepts and practical applications of artificial intelligence and machine learning in healthcare. As AI continues to transform the healthcare industry, understanding how these technologies can address clinical challenges and improve patient outcomes has become increasingly important. Through the use of generative AI tools, this program equips learners with the foundational knowledge needed to explore the growing role of AI in modern medicine.
In this course, you'll explore the unique characteristics of healthcare data, ethical considerations in medical AI, and generative AI learning techniques designed specifically for healthcare applications. You'll also gain practical experience by working with real-world healthcare datasets, helping you understand how AI can be applied in clinical settings to solve real challenges.
As you progress, you'll learn about the regulatory frameworks shaping the use of AI in healthcare, developing a well-rounded understanding of both the technical and responsible use of AI in medicine. By the end of the program, you'll have a strong foundation in healthcare AI and the knowledge to confidently explore its real-world applications.
This course comprises two purposely designed modules that take you on a carefully defined learning journey.
It is an instructor-led course which runs to a fixed schedule, with set start and finish dates. It is driven forward by your instructor and features live sessions that are aired at a set time. You will, however, have time to complete certain activities at your own pace outside of the live sessions.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include reading material, hands-on labs, and online exam questions.
As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues.
Once you have successfully completed the course, you will earn your Certificate of Completion.
By the end of this course, you will be able to:
- Describe how AI and machine learning are transforming healthcare delivery, clinical workflows, and patient outcomes.
- Explain ethical frameworks, regulations, and governance standards relevant to AI in healthcare.
- Summarize common challenges and solutions related to bias, privacy, and AI integration in healthcare.
- Design and implement machine learning workflows tailored to healthcare datasets and requirements.
- Apply generative AI concepts, data, and ethical principles to healthcare use cases.
- Complete a scenario-based project using AI methods on real-world clinical data.
- Evaluate your learning progress and identify opportunities for further development in AI for healthcare.
- Demonstrate readiness for advanced AI topics through a summative assessment and peer-reviewed project.
- Nursing
- Radiology & Imaging Support Staff
- Healthcare administrators
- Health Information Managers
- Healthcare quality & compliance officers
- Healthcare analysts, and researchers
- Professional experience in healthcare
- Basic understanding of statistics concepts
- Familiarity with basic generative AI programs
- General awareness of healthcare data and terminology (helpful but not required)
Course Outline
Video: Foundations of AI in Healthcare Introduction (3:30)
Reading: Foundations of AI in Healthcare Overview
Reading: Learning Objectives and Syllabus
Reading: Grading Scheme
Reading: How to Make the Most of This Course
Reading: Hardware and Software Requirements
Reading: Topic Introduction and Learning Objectives
Video: AI Evolution in Medicine: From Expert Systems to Deep Learning (4:08)
Reading: Enablers of Modern Medical AI
Graded Activity: Connect the Pieces – Enablers of Healthcare AI
Video: AI and ML Fundamentals for Healthcare (4:50)
Video: Neural Networks and Deep Learning in Medicine (4:34)
Reading: Machine Learning Applications in Clinical Practice
Graded Lab: Applying Generative AI in Clinical Practice
Video: AI in Diagnostics and Clinical Decision Support (4:58)
Video: AI in Treatment and Patient Care (5:31)
Graded Activity: Guiding a Hospital on AI Implementation
Topic 1 Practice Quiz: Introduction to AI in Healthcare
Reading: Topic Summary: Introduction to AI in Healthcare
Topic 1 Graded Quiz: Introduction to AI in Healthcare
Reading: Topic 2 Introduction and Learning Objectives
Video: Medical Ethics in the Age of AI (6:00)
Graded Activity: Navigating Medical Ethics in the Age of AI
Video: Bias, Equity, and Fairness in Healthcare AI (6:33)
Video: Ethical Decision-Making Frameworks for AI (6:15)
Graded Lab: Detecting Bias in Medical Datasets
Graded Activity: The Hidden Bias in Your Data
Video: Privacy-Preserving AI Technologies (5:55)
Video: Cybersecurity Framework for Medical AI (5:41)
Reading: Global Regulatory Landscape for Medical AI
Video: Risk Management and Quality Assurance (4:22)
Reading: Regulatory Compliance Checklist
Graded Activity: Privacy, Cybersecurity, and Regulatory Frameworks
Module 2 Practice Quiz: Ethics, Regulation, and Responsible AI in Healthcare
Reading: Topic 2 Summary: Ethics, Regulation, and Responsible AI in Healthcare
Graded Quiz: Ethics, Regulation, and Responsible AI in Healthcare
Reading: Topic Introduction and Learning Objectives
Video: ML Workflow and Healthcare Applications (5:37)
Video: Supervised vs. Unsupervised Learning in Healthcare (4:43)
Reading: Advanced ML Techniques for Medical Data
Video: Feature Engineering for Medical Data (4:54)
Video: Model Validation in Healthcare Settings (4:32)
Graded Lab: Performance Metrics for Medical AI
Video: Clinical Decision Support Integration (4:34)
Video: Future of AI in Healthcare (4:54)
Reading: Implementation Success Factors
Grade Lab: Building a Healthcare Diagnostic Prediction Model
Graded Activity: Detecting Sepsis Before It’s Too Late
Topic 3 Practice Quiz: Machine Learning Applications in Healthcare
Reading: Topic Summary: Machine Learning Applications in Healthcare
Unit
Module 3 Topic Quiz: Machine Learning Applications in Healthcare
Reading: Topic 4 Introduction and Learning Objectives
Graded Lab: Final Project: Designing an AI Solution for Early Liver Disease Detection
Video: Course Summary (3:51)
Reading: Course Glossary
Final Exam
Next Steps
Video: Course Introduction (3:27)
Reading: Part 2 Overview
Reading: Learning Objectives and Syllabus
Reading: How to Make the Most of this Part
Reading: Grading Scheme
Reading: Topic Introduction and Objectives
Video: Foundations of NLP in Healthcare (5:10)
Video: Transformer Architectures in Healthcare NLP (7:06)
Reading: Natural Language Processing in Clinical Practice
Graded Lab: Transforming Clinical Notes with Generative AI
Graded Activity: Applying NLP to Solve Healthcare Challenges
Video: Advanced Information Extraction from Clinical Narratives (5:37)
Video: Medical Coding and Classification with NLP (5:24)
Video: NLP-Powered Clinical Decision Support Systems (5:30)
Graded Lab: Clinical Documentation Analysis Using Generative AI to Structure Clinical Notes
Reading: Automated Medical Coding: Methods and Clinical Implementation
Graded Lab: Clinical Information Extraction Pipeline
Reading: Module Summary: Natural Language Processing for Clinical Data
Graded Quiz: Natural Language Processing for Clinical Data
Reading: Topic Introduction and Objectives
Video: Introduction to LLMs and Prompt Engineering for Medical AI Systems (5:13)
Video: Fine-Tuning LLMs for Healthcare Applications (5:19)
Reading: Large Language Models in Medicine: Opportunities and Challenges
Video: AI-Generated Radiology and Pathology Reports (5:16)
Video: Clinical Decision Support with Generative AI (5:11)
Reading: Evaluation and Validation of AI-Generated Medical Content
Graded Lab: Medical Report Generation Using Generative AI & Excel
Video: Healthcare Chatbots and Virtual Assistants
Graded Lab: Build a Patient Education Chatbot with Generative AI
Practice Quiz: Generative AI for Medical Content and Decision Support
Reading: Topic 2 Summary: Generative AI for Medical Content and Decision Support
Graded Quiz: Generative AI for Medical Content and Decision Support
Reading: Topic Introduction and Objectives
Video: Deep Learning Architectures for Medical Imaging (5:04)
Video: Multi-Modal Medical Image Analysis (5:23)
Reading: Deep Learning in Medical Imaging: Current State and Future Directions
Graded Activity: The Imaging Innovation Challenge
Video: Advanced Segmentation Techniques in Medical Imaging (5:04)
Video: Real-Time Medical Image Analysis and Monitoring (6:30)
Reading: Performance Metrics and Validation in Medical Image Segmentation
Graded Lab: Evaluating Medical Image Segmentation with Generative AI
Video: Multimodal AI: Combining Imaging, Text, and Clinical Data (5:31)
Practice Quiz: Computer Vision and Multimodal AI in Medical Imaging
Reading: Topic Summary: Computer Vision and Multimodal AI in Medical Imaging
Graded Quiz: Computer Vision and Multimodal AI in Medical Imaging
Reading: Topic Introduction and Objectives
Final Project: Generative AI-based Medical Chatbot Application
Reading: Course Glossary
Part 2 Final Exam: AI Technologies in Healthcare
Reading: Congratulations and Next Steps
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
You will explore how generative AI for healthcare supports decision-making, streamlines documentation, and improves communication. The course introduces practical applications, ethical considerations, and real-world use cases that help healthcare professionals integrate AI into everyday practice.
This AI for healthcare professionals course is beneficial for clinicians, nurses, administrators, allied health staff, and healthcare leaders who want to understand how AI can improve efficiency, collaboration, and patient-focused outcomes.
No. The course begins with foundational concepts before progressing to practical applications. Whether you are new to AI or expanding your knowledge, the content is structured to build confidence through clear explanations and relevant healthcare examples.
You will discover how AI assists with information analysis, communication, and decision support while keeping healthcare professionals in control. Learn how AI in patient care can enhance efficiency without replacing clinical expertise or human judgment.
Yes. You will learn how to use AI for clinical documentation and how to simplify note creation and reduce administrative tasks while improving the clinical workflow. This course will allow healthcare teams to dedicate more time to meaningful patient interactions.
Yes. Learners explore practical healthcare scenarios that demonstrate how AI supports decision-making, communication, and operational efficiency. The emphasis is on applying concepts in realistic situations that reflect everyday professional responsibilities.
The course introduces widely used concepts behind healthcare AI tools and explains how they fit into healthcare environments. You will understand their capabilities, limitations, and considerations for responsible implementation in professional settings.
You will examine how healthcare datasets influence AI outputs while exploring privacy, bias, transparency, and ethical decision-making. The course emphasizes responsible AI use to support safe, reliable, and compliant healthcare practices.
Understanding AI helps you stay informed about emerging technologies transforming healthcare. You'll develop practical knowledge that supports informed decision-making, improved collaboration, and greater confidence when working alongside AI-powered solutions.
The course explores how AI enhances communication, reduces repetitive work, and supports informed decisions that strengthen healthcare delivery. You will gain practical insights into balancing innovation with patient safety, ethics, and professional responsibility.
Type of certificate
Certificate of Completion
Credly Badge
About this course
02 Modules
08 Skills
Includes
12 Hands-on labs
01 Capstone project
08 Self-paced activities
06 Quizzes
02 Mid-course exams
02 Final exams
Create
AI-powered clinical decision support & diagnosis
ML models for healthcare data & bias evaluation
Generative AI for clinical documentation & reports
AI solutions for patient education & chatbots
Capstone project solving a real healthcare challenge
Exercises to explore
ChatGPT
Claude
MS CoPilot
Goolge Gemini & DALL-E
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