Course Description
This course examines how artificial intelligence transforms healthcare across pharmacy, medicine, veterinary medicine, and nursing. Students explore AI tools, ethical implications, and case studies to understand applications in diagnostics, clinical decision-making, and patient care.
Additional Requirements for Graduate Students:
Extended Literature Review
For the Week 4 assignment on AI in medical diagnostics, graduate students must include at least three peer-reviewed journal articles and provide a critical synthesis of the methodologies and findings.
Graduate students will present a 10-minute scholarly briefing during Week 14, focusing on the theoretical or methodological implications of their final project. This is in addition to the team presentation.
Athena Title
Artificial Intelligence in Hea
Undergraduate Pre or Corequisite
BIOS 2010 or BIOS 2010E or STAT 2000 or STAT 2000E or VPHY(PHRM) 4350/6350 or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
- Students will develop foundational knowledge of artificial intelligence (AI) and its role in modern healthcare.
- Students will build an interdisciplinary perspective to analyze and contribute to decisions involving AI in clinical, pharmaceutical, veterinary, and nursing contexts.
- Students will understand the principles, applications, and limitations of AI across health professions.
- Students will engage in collaborative problem-solving using real-world healthcare scenarios.
Topical Outline
- Week 1: Introduction to AI in Healthcare
Overview of AI fundamentals
Interdisciplinary scope across health professions
Reflection on AI relevance in students' fields
- Week 2: AI Concepts & Methods
Machine learning, deep learning
Natural language processing (NLP), computer vision
Retrieval-Augmented Generation (RAG), Federated Learning
Artificial Neural Networks (ANNs), Decision Trees
- Week 3: AI in Pharmacy
Drug discovery and development
Medication adherence and reconciliation
Pharmacovigilance and safety monitoring
- Week 4: AI in Medicine
Diagnostic imaging and interpretation
Predictive analytics for patient outcomes
Clinical decision support systems (CDSS)
- Week 5: AI in Veterinary Medicine
Animal health monitoring and diagnostics
Herd management and predictive modeling
Comparative analysis with human healthcare
- Week 6: AI in Nursing
Patient monitoring and triage
Workflow optimization and staffing
Care planning and documentation
- Week 7: Data in Healthcare
Electronic Health Records (EHRs)
Interoperability and data standards
Data quality, bias, and ethical concerns
- Week 8: Clinical Decision Support Systems
CDSS architecture and design
Use cases across disciplines
Workflow development exercises
- Week 9: Natural Language Processing in Healthcare
Text mining and chatbot applications
Documentation automation and summarization
- Week 11: Regulatory & Legal Frameworks
HIPAA, GDPR, and data governance
FDA-approved AI tools
Veterinary data standards
- Week 12: AI Tools & Platforms
IBM Watson, BioGPT, VetAI, ChatGPT
Tool comparison and evaluation
- Week 13: Capstone Planning & Peer Review
Project development and feedback
Draft proposal submission
- Week 14: Capstone Presentations
Team presentations of AI solutions
Interdisciplinary feedback and critique
- Week 15: Course Wrap-Up & Future Directions
Reflection on learning and career pathways
Emerging trends in AI and healthcare
