705.617.81 - Artificial Intelligence in Healthcare

Course Structure

The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

1. History of AI Development 2. Medical Ethics and AI 3. Common AI Applications in Healthcare 4. Common AI Applications in Healthcare

1. What is AI? 2. Healthcare is Unique 3. The R.O.A.D Management Framework 4. Case Study

1. What is an Algorithm? 2. High Level Overview of 6 Algorithms - (Regression, SVM, NB, DT, RF, NN) 3. Measuring Performance - (Accuracy, prec., recall, F1, specificity, ROC) 4. (Chapter 3) Evaluation - The Facts Matter: Pseudo-Innovation vs. Real Innovation 5. Case Study

1. The Human Baseline 2. Modeling - An overview of predictive modeling, neural networks, and deep learning 3. Case Studies

1. Large Language Models (LLMs) 2. How LLMs can be used in healthcare to save time and reduce burnout 3. Areas of healthcare where LLM are not reliable - yet - 4. Case Study

1. Automation in healthcare 2. Robotic Assisted Surgery 3. Photoacoustics and machine learning 4. Case Study

1. Co-Morbidity and Complex Interactions 2. Epidemiology - (Markov models, S-E-I-R, Time-series, seasonal trends) 3. Case Studies

1. Precision medicine 2. Prevention 3. Case Study

Review an academic paper or case study that applies AI in the Health domain. Provide a critical review. Discuss with classmates

1. Fairness - Addressing the Ethics, regulatory, and Privacy Issues 2. Electronic Health Records (EHR) 3. AI Regulation 4. Case study

1. Formal and Informal Leadership 2. Social Network Based Change Management 3. Scaling from pilot to hospital-wide adoption 4. Synergy - Building a Successful Clinician-Computer Collaboration 5. Case Study

1. Publishing in medical journals

1. Revisit R.O.A.D Management Framework 2. Why Do AI Projects Fail and What to Do About It? 3. Resistance - understanding and overcoming the resistance to AI, randomization and change. 4. Execution - increasing the odds of future success. 5. Integration - Building a alearning healthcare system with pragmatic AI trials. 6. Streamlining - Reducing waste and lowering costs in health care. 7. Case Study

1. Capstone Project

Course Goals

By the end of this course, students will be able to:

1. Explain foundational AI concepts and healthcare applications
   Articulate the history, development, and current uses of artificial intelligence in healthcare, including key algorithms, evaluation metrics, and unique challenges of clinical environments.

2. Evaluate ethical, regulatory, and fairness considerations
   Critically assess issues related to medical ethics, patient privacy, bias, and compliance frameworks in the development and deployment of healthcare AI systems.

3. Apply predictive and diagnostic modeling techniques
   Build, test, and interpret machine learning models (e.g., regression, SVMs, neural networks) for clinical decision support, risk scoring, and diagnostic assistance.

4. Integrate large language models and automation into workflows
   Demonstrate how LLMs, robotic process automation, and related AI technologies can be responsibly used to improve efficiency, reduce burnout, and streamline clinical and operational tasks.

5. Analyze complex healthcare dynamics with AI methods
   Use advanced techniques such as epidemiological modeling, precision medicine approaches, and multi-morbidity analysis to support prevention and population health management.

6. Design and critique AI implementations in healthcare
   Evaluate published studies and case examples, identifying strengths, limitations, and opportunities for innovation in AI-driven healthcare systems.

7. Develop leadership and change management strategies
   Apply principles of formal/informal leadership, social network analysis, and organizational adoption to scale AI solutions from pilot projects to hospital-wide systems.

8. Communicate AI research and outcomes effectively
   Produce and present professional-quality analyses, reports, and presentations aligned with medical journal and stakeholder expectations.

9. Create a portfolio-ready healthcare AI solution
   Collaboratively design, implement, and present a capstone project that translates AI capabilities into an integrated, ethical, and practical solution for a real-world healthcare workflow.

Textbooks

No textbook is required for this course.

Student Coursework Requirements

 

• Quizzes (Modules 1–3) – 15%
  Short quizzes to assess comprehension of early foundational concepts.

• Problem Set (Module 3) – 10%
  Applied assignment on algorithm evaluation metrics and model performance.

• Assignments (Modules 4–7, 10–12) – 30%
  Several applied assignments on predictive modeling, LLMs, automation, epidemiology, ethics/regulation, and publishing.

• Midterm Exam (Module 8) – 20%
  Covers concepts and applications from the first half of the course.

• Paper / Case Study Review (Module 9) – 10%
  Critical written review of an academic paper or case study.

• Capstone Project & Final Presentation (Module 14) – 15%
  Team-based project and final presentation applying AI in a healthcare workflow.

Grading Policy

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Score Range	Letter Grade
100-97	= A+
<97-93	= A
<93-90	= A−
<90-87	= B+
<87-83	= B
<83-80	= B−
<80-77	= C+
<77-73	= C
<73-70	= C−
<70-67	= D+
<67-63	= D
<63	= F

Academic Policies

Deadlines for Adding, Dropping, and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course. 

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students. This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. Students with disabilities (including those with psychological conditions, medical conditions, and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.  For further information or to start the process of requesting accommodations, please contact EP Student Disability Services at ep-disability-svcs@jhu.edu. 

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. For a full description of the code please visit the Student Conduct Code website.

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.