The 2024 CMLH Fellowships in Generative AI in Healthcare Call for Proposals is open!

The Center for Machine Learning and Health is pleased to announce a fall 2024 call for fellowship proposals. 

We invite applications for the Center for Machine Learning and Health (CMLH) Fellowships in Generative AI in Healthcare. Each fellowship provides full support for one year for a Ph.D. student at Carnegie Mellon University who is pursuing cutting-edge research in this area.

Submissions should propose a project that is within the following theme: Generative AI in Healthcare.

Generative AI in Healthcare

Generative AI in healthcare refers to the application of advanced artificial intelligence techniques to create, generate or simulate medical data, insights or content. By enabling more accurate diagnostics, personalized treatments and efficient research, generative AI is transforming the way healthcare is delivered and advancing medicine. It can help reduce the burden of documentation for healthcare workers and add automation to administrative functions. The promise of AI with improvements for patient data and privacy allows for more focus on caring for patients and improving efficiencies and more visibility to clinical decisions.

Fellowship Award Details

Each fellowship will provide support for one year for a Ph.D. student at Carnegie Mellon whose research, in collaboration with a faculty member, advances generative AI in healthcare. It provides:

• One year of tuition and stipend support (including summer stipend) for the student (nontransferable).
• $3,000 in funding to support the underlying research, including conference travel for paper presentation, equipment and human-subject experiments. 

This call includes a preliminary step with an Intent To Propose (ITP) submission. ITPs that are selected will advance to a full proposal submission. 

Criteria and Eligibility

Who is Eligible?

• Full-time, currently enrolled Ph.D. students at Carnegie Mellon University.
• Students with a primary research project related to Generative AI in healthcare.

Criteria

• Students must be enrolled in the Ph.D. program for a full academic year for the duration of the fellowship.
• We welcome projects at any stage, both initial and mature. 
• No other fellowship should support the student throughout the duration of the award. The CMLH Fellowship cannot be combined with any other fellowships. 

Format of the Intent to Propose Applications

All ITP submissions should follow the format described below. Submissions that do not follow the format will not be accepted.

The application should be submitted via email attachment to cmlh@cs.cmu.edu. It should consist of a single page PDF file, separated into the three sections described below. Text should be single-spaced, 11pt font. Please mark the page as "Confidential."

Bibliographies, CVs, images or charts should not be included in the ITP. If the ITP is selected to advance to a full proposal, additional information and documents, including a CV and a longer proposal, will be required.

Section I: Student and Research Information

1. Student name, email, college/school and department.
2. Name of Ph.D. program in which the student is currently enrolled at CMU.
3. Faculty adviser(s).
4. Proposal title.

Section II:  Pitch Statement — ONE SENTENCE

This is an elevator pitch for your project: how you'd describe the project if you had just a few seconds in an elevator with a funder. It should be a brief, persuasive sentence that you would use to create excitement around your project. It should be interesting, memorable and succinct. 

Section III:  The Research — 1/2 PAGE MAXIMUM

This abstract should describe your proposed project; its importance and potential impact; and why it is a novel, innovative or unique approach to the problem. You should be sure to answer the following questions (though you do not need to have separate sections for each question, and the answers should be integrated into the half-page narrative).

Why is it important in healthcare?

• What is the problem and why is it important?
• What is your proposed solution?

What is the innovation?

• What new technique, idea, approach or method is proposed to solve the stated problem? 
• How does it relate to the theme of Generative AI?

What will the impact be on healthcare?

• What is the goodness/impact that your innovation will provide? 
• How will it deliver better outcomes or more efficiency or both? 

Contact the CMLH with any questions.