AI Course for Medical Practitioners
About
M.S. Data Science
Program Structure
Why Choose Our M.S. Data Science Program?
Classroom Program
With personalized student and faculty interaction in a live classroom environment.
Career-Focused Flexibility
Evening online classes designed for working professionals
Industry-Ready Skills
Master machine learning, statistical inference, and data visualization
Hands-on Projects
Apply concepts to projects using real data.
Expert Faculty
Learn from faculty who are passionate about research in cancer genomics, cybersecurity, human impacts of energy systems, and healthcare analytics
Enterprise-Grade Resources
Access high-performance computing and advanced software tools
Year 1 Fall
Fall Semester 1
Applied Artificial Intelligence in Healthcare
AI Fundamentals in Healthcare
Description: This course provides an introduction to artificial intelligence principles and methodologies with a focus on healthcare applications. Students will explore the role of AI in clinical decision support, diagnostics, and patient engagement.
Objectives:
- Understand the core concepts of AI and its subfields, including machine learning and deep learning.
- Examine real-world applications of AI in healthcare and public health.
- Analyze ethical considerations and regulatory frameworks surrounding AI deployment in medical settings.
Healthcare Data Engineering and Management
Description: This course covers data architecture, storage, and interoperability in healthcare, focusing on the development of scalable data pipelines for AI applications.
Objectives:
- Develop expertise in database design and management for healthcare data.
- Implement Extract, Transform, Load (ETL) processes for healthcare data integration.
- Ensure compliance with healthcare regulations such as HIPAA and GDPR in data management.
AI Model Development in Healthcare
Description: This course introduces AI model development in healthcare, covering data preprocessing, algorithm selection, and model evaluation.
Objectives:
- Develop a strong theoretical and practical understanding of machine learning models.
- Train and evaluate AI models using real-world healthcare datasets.
- Address bias and fairness in AI model development.
Spring Semester 1
Applied Artificial Intelligence in Healthcare
Biostatistics and Data Analysis in Healthcare
Description: This course covers statistical methods for analyzing healthcare data, emphasizing predictive analytics and statistical modeling.
Objectives:
- Apply statistical methods to healthcare datasets.
- Visualize and interpret data to support clinical and operational decision-making.
- Evaluate data-driven insights for improving patient outcomes.
Interoperability and AI Integration in Healthcare
Description: This course explores the principles of healthcare data exchange, interoperability standards, and AI-driven automation in clinical settings.
Objectives:
- Understand HL7, FHIR, and other interoperability frameworks.
- Implement AI solutions that integrate with electronic health record (EHR) systems.
- Ensure compliance with data privacy and security standards.
Natural Language Processing (NLP) and AI in Clinical Documentation
Description: This course explores how NLP techniques are applied to analyze clinical notes, medical literature, and patient narratives.
Objectives:
- Understand NLP methodologies and their applications in healthcare.
- Develop AI-driven solutions for automated medical coding and clinical summarization.
- Address ethical concerns in the use of patient-generated text data.
Summer Semester 1
AI-Powered Health and Medical Practitioners Informatics Program
Applied AI Internship in Healthcare
Description: Students gain hands-on experience by applying AI solutions in real-world healthcare settings, collaborating with industry professionals.
- Apply AI methodologies to solve practical clinical and operational challenges.
- Work with interdisciplinary teams in healthcare organizations.
- Deliver a project report with actionable recommendations.
Year 2
Fall Semester 2
AI-Powered Health and Medical Practitioners Informatics Program
Ethics, Regulation, and AI Governance in Healthcare
Description: This course covers ethical concerns, bias mitigation, and regulatory frameworks governing AI applications in healthcare.
Objectives:
- Navigate legal and ethical challenges in healthcare AI implementation.
- Assess bias and fairness in AI algorithms.
- Develop policies for responsible AI governance.
Clinical Decision Support Systems and Generative AI in Healthcare
- Evaluate AI-powered decision-support systems for clinicians.
- Explore generative AI applications in clinical documentation and patient engagement.
- Develop strategies for AI adoption in clinical settings.
Human-Centered AI and UX Design for Healthcare Applications
Description: This course focuses on user experience (UX) design principles for AI applications in healthcare, ensuring usability for patients and clinicians.
Objectives:
- Design AI-driven tools with a focus on accessibility and usability.
- Conduct user research and usability testing for AI applications.
- Evaluate the impact of AI tools on patient experience and clinical workflows.
Spring Semester 2
Capstone Internship in AI and Healthcare
Description: A culminating experience where students apply AI methodologies to address a healthcare challenge, resulting in a final project and presentation.
Objectives:
- Implement AI solutions in a real-world healthcare setting.
- Collaborate with interdisciplinary teams.
- Present findings to industry stakeholders or publish in peer-reviewed forums.