AI CoLab AI in Healthcare Internship
The AI CoLab AI in Healthcare Internship Program trains students and early-career professionals in applied Artificial Intelligence (AI), machine learning, clinical informatics, bioinformatics, biostatistics, and research operations with exposure to real-world research including our active federal research portfolio at MedStar Health and Georgetown University. Interns work on research projects with publicly available data, contributing to ongoing AI healthcare research across the full study lifecycle, from research question development and data analysis through dissemination.
We offer 3 program tracks designed to meet you where you are:
- AI in Healthcare Internship Program: a 9-12-week in-person summer cohort for high school, undergraduate, graduate, post-doctoral, and early career professionals seeking hands-on exposure to team science and applied AI healthcare research. Cohort sizes range from 25-50 students.
- Capstone Internship: A semester-long advanced program for graduate students, medical students, and early-career professionals focused on competency development in applied AI research or research operations. Cohort sizes range from 1-5 students.
- AI CoLab Fellowship: A full-year (12 months) program for advanced graduate students, medical students, and early-career professionals pursuing independent research training and leadership development. Cohort sizes range from 1-5 students.
To date, 206 interns from 68 institutions have completed an AI CoLab internship.
Compare Tracks at a Glance
AI Healthcare Internship | Capstone Internship | AI CoLab Fellowship | |
|---|---|---|---|
| Duration | 9-12 weeks | 1 semester | Full Year (12 months) |
| When | Summer | Fall, Spring, Summer | Full Year |
| Who | High School through PhD / MD | Graduate, medical students, early-career professionals | Bachelor’s degree and above, early-career investigators |
| Focus | Exposure to AI healthcare research | Applied AI competency development | Independent research & leadership |
| Time Commitment | 20 hours / week | 20 hours / week | 20 hours / week |
| Cohort Size | 25-50 interns | up to 5 interns | up to 5 fellows |
| Key Deliverable | Group research poster & individual portfolio | Individual poster & manuscript (preferred) | Proposal, Poster, and manuscript (strongly preferred) |
AI Healthcare Internship Program Options
AI in Healthcare Internship | Flagship Program
Summer: 9-12 weeks | up to 50 interns per cohort
The flagship program. Interns work in cohort teams on real AI healthcare research projects, mentored by subject matter experts from MedStar Health and Georgetown University. Designed to reflect what team science and AI healthcare research actually look like in practice from research question development through poster presentation. After completion of the program, interns will receive a MedStar Health & Georgetown University AI CoLab Certificate of completion
Format: in-person
Commitment: 20 hours / week
Mentorship: Group mentor + individual mentor
What you will produce:
- Group research poster presented at program close & at AI CoLab Annual Scientific Meeting
- Individual project portfolio and 1-2 page summary
- Weekly progress presentations to full cohort at all-hands meetings
Capstone Internship
Fall, Spring, or Summer | up to 5 capstone interns per cohort
A semester-long advanced program for participants who are ready to go deeper. Focused on applied AI research competency development or research operations, depending on career trajectory. Each participant is individually matched with an AI CoLab mentor and works toward a publishable-quality deliverable. After completion of the program, interns will receive a MedStar Health & Georgetown University AI CoLab Certificate of completion.
Format: virtual or hybrid
Commitment: 20 hours / week
Mentorship: Individual mentor
What you will produce:
- Individual research poster presented at AI CoLab Annual Scientific Meeting
- Individual project portfolio
- Manuscript prepared for submission (preferred)
AI CoLab Fellowship
12-month program | up to 5 AI CoLab fellows
A postdoc-style research fellowship for graduate, early-career investigators, and advanced students pursuing independent research training and leadership development. Fellows carry their own research agenda, supported by an individual PI-style mentor and a combined AI CoLab and as needed external advisory mentors. A manuscript submitted for publication is strongly encouraged, and an individual poster is required for completion.
Format: virtual or hybrid
Commitment: 20 hours / week
Mentorship: Individual mentor, additional SME mentors as needed
What you will produce:
- Research proposal
- Individual research poster (required) – must be presented at a scientific conference
- Independent projects & ad-hoc assignments
- Manuscript prepared for submission (strongly encouraged)
FAQ
Yes. Past participants have come from 68 institutions across the United States and internationally, Students from many institutions including Johns Hopkins, NYU, Vanderbilt, Stanford, George Mason University, George Washington University, and the University of Maryland, among many others have joined our program. The actual percentage fluctuates cohort-to-cohort, but overall, Georgetown University students represent approximately 20% of all participants.
Projects apply methods including machine learning, natural language processing (NLP), predictive modeling, and data analysis to clinically grounded research questions using publicly available datasets. Examples of past projects include retrieval-augmented generation (RAG) based clinical language models, diagnostic prediction models, and AI-driven patient feedback analysis tools. Capstone and Fellowship participants work on independent, publishable-quality research with individual PI-style mentors.
Participants who successfully complete the program receive a joint certificate of completion from MedStar Health and Georgetown University (AI CoLab) and are eligible to request letters of recommendation from program mentors. All graduates join the AI CoLab alumni network.
The program welcomes applicants from a wide range of disciplines including computer science, biomedical informatics, data science, public health, nursing, medicine, public administration, and business. Prior experience with Python, R, or SQL is helpful for technical project tracks but is not required for all roles. Strong candidates across all tracks are self-directed, collaborative, and genuinely curious about AI in healthcare. Students applying to capstone and fellowship tracks should have experience with research and bring advanced skills in one or more areas of focus (e.g., clinical, AI / ML / technical knowledge, research).