AI Course for Medical Practitioners
About
M.S. Data Science
Why Choose Our M.S. Data Science Program?
Live, online, evening classes
With the same student and faculty interaction as in-person classroom
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
AI-Powered Health and Medical Practitioners Informatics Program
AI and Machine Learning in Healthcare
Description: This course provides an overview of Artificial Intelligence (AI) and Machine Learning (ML) principles, with a focus on their applications in healthcare. Students will explore how these technologies are transforming diagnostics, treatment planning, and operational efficiency in clinical settings.
Objectives:
- Understand the foundational principles of AI and ML in healthcare, including supervised, unsupervised, and reinforcement learning.
- Examine the ethical, legal, and regulatory considerations in deploying AI and ML solutions.
- Evaluate the potential of AI to improve clinical workflows and decision support systems.
- Apply AI and ML techniques to real-world healthcare scenarios to address challenges and improve outcomes.
Data Management and Interoperability in Healthcare
Description: This course explores the principles of data management and interoperability in healthcare, focusing on strategies for ensuring seamless data exchange across systems. Topics include electronic health records (EHR), data integration, and compliance with industry standards.
Objectives:
- Understand healthcare data standards such as HL7, FHIR, and their role in achieving interoperability.
- Explore strategies for ensuring data integrity, privacy, and security, with a focus on HIPAA compliance.
- Evaluate methods for improving data quality and enabling actionable insights in clinical and operational workflows.
- Address challenges in merging diverse datasets for improved decision-making in healthcare settings.
Health Informatics and Biostatistics
Description: This course introduces statistical methods and their applications in health informatics. Students will learn to visualize data, conduct analyses, and interpret findings to inform clinical and public health decision-making.
Objectives:
- Â Apply descriptive and inferential statistical methods to analyze healthcare data.
- Explore data visualization techniques to present findings effectively to stakeholders.
- Develop skills to conduct biostatistical analyses in clinical trials, public health studies, and patient outcome research.
- Critically evaluate data to support evidence-based decision-making in healthcare.
Year 1 Winter
AI-Powered Health and Medical Practitioners Informatics Program
Deep Learning and Natural Language Processing (NLP) in Healthcare
Description: This course explores advanced AI techniques like deep learning architectures (Convoluted Neural Networks, Reoccurring Neural Networks) and their applications in healthcare. Students will examine clinical language processing, automated medical coding, EHR data mining, and text-based AI models.
Objectives:
- Understand the principles of deep learning and NLP.
- Apply AI models to process and analyze clinical data.
- Explore ethical considerations in using patient data for AI development.
Predictive Analytics and Risk Modeling in Healthcare
Predictive Analytics and Risk Modeling in Healthcare Description: Focused on predictive analytics, this course teaches students how to forecast outcomes and personalize patient care through data analysis and machine learning models.
Objectives:
- Develop predictive models for early disease detection and risk stratification.
- Analyze data to create personalized treatment strategies.
- Integrate nursing informatics to enhance predictive care in clinical workflows.
Clinical Decision Support Systems (CDSS) and AI with Agentic & Generative AI Integration
Description: This course explores AI-driven clinical decision support systems (CDSS), emphasizing their integration into healthcare workflows to improve patient outcomes. The course incorporates the emerging role of Agentic AI & Generative AI (GenAI) in creating dynamic, context-aware recommendations and aiding healthcare professionals in decision-making.
Objectives:
- Assess the design, implementation, and evaluation of CDSS tools, including their integration with existing systems like EHRs.
- Explore the potential of Agentic AI & Generative AI to provide personalized treatment suggestions, summarize patient histories, and simulate clinical scenarios
- Leverage medical practitioner’s expertise to develop and refine decision-support technologies tailored to patient and clinician needs.
Year 1 Spring
AI-Powered Health and Medical Practitioners Informatics Program
Ethics, Bias, and Legal Considerations in AI/ML for Healthcare
Description: This course examines the ethical, legal, and social implications of using AI in healthcare, emphasizing fairness, regulatory compliance, and societal impacts.
Objectives:
- Identify ethical challenges in healthcare AI implementation.
- Navigate regulatory frameworks (e.g., FDA, GDPR, HIPAA).
- Promote equitable AI solutions, focusing on nursing and patient advocacy.
AI Systems Integration and Deployment in Healthcare
Description: Students learn to deploy AI solutions effectively within healthcare systems, addressing challenges in scalability, user adoption, and interoperability.
Objectives:
- Design integration strategies for AI systems in clinical settings.
- Solve interoperability issues using HL7 and FHIR standards.
- Incorporate nursing informatics for smooth AI deployment.
Human-Computer Interaction (HCI) and User Experience (UX) Design in Healthcare AI
Description: This course focuses on designing intuitive AI interfaces to improve user engagement and satisfaction in healthcare settings.
Objectives:
- Develop user-friendly AI tools tailored to clinicians and patients.
- Evaluate UX in medical practitioners-specific AI applications.
- Explore patient engagement technologies in digital health.
Year 2 Summer
AI-Powered Health and Medical Practitioners Informatics Program
Internship and Capstone: Applied AI/ML Projects in Healthcare
Description: Students work on real-world AI/ML projects in collaboration with healthcare organizations, applying their knowledge to solve practical challenges.
Objectives:
- Apply AI concepts to address clinical, operational, or administrative problems.
- Develop actionable insights using medical practitioners informatics principles.
- Deliver a comprehensive project report and presentation.
Year 2 Fall
AI-Powered Health and Medical Practitioners Informatics Program
Internship and Capstone: Applied AI/ML Projects in Healthcare (Continuation)
Description: Building on the summer capstone, students finalize their projects and prepare for professional presentations and publications.
Objectives:
- Refine and implement AI/ML solutions for healthcare challenges.
- Collaborate with interdisciplinary teams, including medical practitioners staff.
- Publish findings in peer-reviewed forums or present at conferences.