COURSE INFORMATION
This CE course provides an in-depth exploration of Artificial Intelligence (AI) as it applies to the modern healthcare ecosystem, grounding learners in both the historical foundations and contemporary realities of AI-enabled healthcare. Drawing directly from the introductory concepts of the course, learners are introduced to AI not as a replacement for human judgment, but as a powerful tool designed to augment clinical, operational, and administrative decision-making. The course emphasizes the evolution of algorithms, data-driven reasoning, and computational thinking as the backbone of modern AI systems, setting the stage for responsible and effective application in healthcare environments.
The introduction frames AI within healthcare as a socio-technical system, one that exists at the intersection of technology, people, processes, and ethics. Learners explore why healthcare presents unique challenges for AI adoption, including data complexity, variability in clinical workflows, patient safety considerations, and the high-stakes nature of medical decision-making. Rather than focusing solely on technical development, the course highlights the importance of understanding context, governance, and human oversight as essential components of successful AI integration.
A central theme of the course is augmented intelligence, underscoring the role of AI as a collaborator that supports clinicians, nurses, administrators, and health system leaders. The introduction establishes how AI can enhance pattern recognition, predictive capability, and operational efficiency while preserving the critical role of human expertise, empathy, and accountability. Learners are encouraged to critically assess both the promise and limitations of AI technologies, developing a balanced perspective grounded in evidence rather than hype.
The course description further reflects the introduction’s emphasis on ethical responsibility, transparency, and trust. Learners are introduced early to key concerns such as data privacy, algorithmic bias, explainability, and patient autonomy. These concepts are positioned not as afterthoughts, but as foundational principles that must guide AI development and deployment across all healthcare settings. By grounding ethical considerations in real-world healthcare contexts, the course prepares learners to anticipate and mitigate unintended consequences of AI adoption.
Ultimately, this course equips learners with a conceptual framework to understand AI’s role in healthcare today and its trajectory for the future. Building on the introduction, the course prepares students and professionals to engage thoughtfully with AI technologies, evaluate their appropriateness for specific healthcare use cases, and participate in informed decision-making as clinicians, administrators, educators, or leaders. The course sets the foundation for deeper exploration of AI applications, governance, and innovation while reinforcing the central principle that effective healthcare AI must remain human-centered, evidence-based, and mission-driven.
Course Code: AI 500. Continuing Education Contact Hours = 50.
Instructor/Course Author: Christian Caicedo, MD, MBA, CPE, FACHE
Link to Resume: access here
TEXTBOOK: There is one required Textbook for this course.
AI in Healthcare from Basics to Breakthroughs. AI simplified. Your guide to the Heart of Healthcare’s Future; Dr. Anjun Ahmed and Dr. Po-Hao Chen.
- ISBN-13: 979-8882846915
Link to Purchase on Amazon.com: click here
Additional Assignments: there are Online Videos that are required for viewing for this course as well. Once enrolled into the course, students are provided with full information regarding Video Viewing and assignments. Videos are NOT required to be purchased.
TIME FRAME: You are allotted two years from the date of enrollment, to complete this course. There are no set time-frames, other than the two year allotted time. If you do not complete the course within the two-year time-frame, you will be removed from the course and an “incomplete” will be recorded for you in our records. Also, if you would like to complete the course after this two-year expiration time, you would need to register and pay the course tuition fee again.
GRADING: You must achieve a passing score of at least 70% to complete this course and receive the 50 hours of awarded continuing education credit. There are no letter grades assigned. You will receive notice of your total % score. Those who score below the minimum of 70% will be contacted by the American Institute of Health Care Professionals and options for completing additional course work to achieve a passing score, will be presented.
BOARD APPROVALS: The American Institute of Health Care Professionals (The Provider) is approved by the California Board of Registered Nurses, Provider number # CEP 15595 for 50 Contact Hours. Access information
This course, which is approved by the Florida State Board Of Nursing (CE Provider # 50-11975) also has the following Board of Nursing Approvals, for 50 contact hours of CE
The American Institute of Health Care Professionals Inc: is a Rule Approved Provider of Continuing Education by the Arkansas Board of Nursing. CE Provider # 50-11975.
The American Institute of Health Care Professionals Inc: is a Rule Approved Provider of Continuing Education by the Georgia Board of Nursing. CE Provider # 50-11975.
The American Institute of Health Care Professionals Inc: is a Rule Approved Provider of Continuing Education by the South Carolina Board of Nursing. CE Provider # 50-11975.
The American Institute of Health Care Professionals Inc: is a Rule Approved Provider of Continuing Education by the West Virginia Board of Examiners for Professional Registered Nurses. CE Provider # 50-11975.
The American Institute of Health Care Professionals Inc: is a Rule Approved Provider of Continuing Education by the New Mexico Board of Nursing. CE Provider # 50-11975.
Course Refund & AIHCP Policies: access here
ONLINE CLASSROOM RESOURCES AND TOOLS
* Examination Access: there is link to take you right to the online examination program where you can print out your examination and work with it. All examinations are formatted as “open book” tests. When you are ready, you can access the exam program at anytime and click in your responses to the questions. Full information is provided in the online classrooms.
* Student Resource Center: there is a link for access to a web page “Student Resource Center.” The Resource Center provides for easy access to all of our policies/procedures and additional information regarding applying for certification. We also have many links to many outside reference sites, such as online libraries that you may freely access.
* Online Evaluation: there is a link in the classroom where you may access the course evaluation. All students completing a course, must, without exception, complete the course evaluation.
* Faculty Access Information: you will have access to your instructor’s online resume/biography, as well as your instructor’s specific contact information.
* Additional Learning Materials: All course handouts are available in the online Video classrooms. All E-Learning Books are available in the classrooms for students to download.
COURSE OBJECTIVES: Upon completion of this course, you will be able to:
- Explain foundational AI concepts and technologies relevant to healthcare
- Analyze clinical, operational, and administrative AI use cases
- Evaluate ethical, legal, and regulatory implications of AI adoption
- Assess AI performance, bias, and impact on patient outcomes
- Apply governance and leadership principles to AI implementation
- Anticipate future trends and workforce implications of AI in healthcare
COURSE CONTENT
Algorithms & AI Foundations
Learning Objective: Explain the historical foundations of algorithms and AI.
Building Blocks of AI
Learning Objective: Differentiate core AI technologies such as ML and neural networks.
Key AI Concepts & Terminology
Learning Objective: Define key AI terminology and learning paradigms.
Impact of AI on Healthcare
Learning Objective: Analyze AI’s impact on diagnostics and care delivery.
Ethics & Patient Privacy
Learning Objective: Evaluate ethical, privacy, and bias considerations in AI.
Foundational AI Technologies
Learning Objective: Describe AI technologies supporting healthcare systems.
AI in Nursing
Learning Objective: Assess AI applications in nursing practice.
AI Integration & Leadership
Learning Objective: Apply leadership principles to AI implementation.
Augmented Intelligence
Learning Objective: Explain human–AI collaboration in healthcare.
AI in Medical Imaging
Learning Objective: Analyze AI applications in medical imaging.
Imaging Case Studies
Learning Objective: Evaluate real-world AI imaging case studies.
Imaging Challenges & Solutions
Learning Objective: Identify challenges and solutions in imaging AI.
AI in Pharmaceutical R&D
Learning Objective: Explain AI applications in drug discovery.
AI in Clinical Trials
Learning Objective: Analyze AI’s role in clinical trials.
Future of Pharmaceuticals
Learning Objective: Assess future pharmacy and medication AI use.
Precision Medicine
Learning Objective: Apply AI to personalized medicine.
Predictive Analytics
Learning Objective: Evaluate predictive analytics and early intervention.
Patient Perspective
Learning Objective: Assess AI from the patient experience perspective.
Healthcare Administration
Learning Objective: Analyze AI in healthcare administration.
Billing & Claims
Learning Objective: Explain AI in billing and claims processing.
Operational Continuity
Learning Objective: Assess AI’s role in operational continuity.
AI + IoT + RPA
Learning Objective: Evaluate integration of AI with IoT and RPA.
Generative AI & NLP
Learning Objective: Analyze generative AI and NLP in healthcare.
Foundational Models & LLMs
Learning Objective: Explain foundational models and LLMs.
Implementing AI into Practice
Learning Objective: Apply validation and integration principles.
AI Governance
Learning Objective: Evaluate AI governance frameworks.
Measuring AI Performance
Learning Objective: Measure AI performance and outcomes.
Navigating the AI Landscape
Learning Objective: Develop professional AI competencies.
Case Studies
Learning Objective: Analyze multidisciplinary AI case studies.
Conferences & Knowledge Sharing
Learning Objective: Evaluate role of conferences in AI advancement.
Regulatory Environments
Learning Objective: Explain AI regulatory and compliance requirements.
Healthcare Workforce
Learning Objective: Assess AI’s impact on the workforce.
Future Directions
Learning Objective: Anticipate future AI trends in healthcare.
Synthesis & Integration
Learning Objective: Synthesize course concepts for responsible AI adoption.