Artificial Intelligence / Machine Learning Demonstration Projects 2025

Crowdsourcing ideas to bring advances in data science, machine learning, and artificial intelligence into real-world clinical practice.

A Learning Healthcare System, as envisioned by the Institute of Medicine and the NIH Collaboratory, aims to bring new knowledge quickly into the healthcare delivery system, including advances in data science, machine learning, and artificial intelligence.   

This is the second call for UCSF’s Artificial Intelligence/Machine Learning Demonstration Projects - “AI Pilots” - aimed at advancing UCSF as a Learning Healthcare System, sponsored by UCSF and UCSF Health Leadership. This also represents the fourth institution-wide Call for Proposals for Learning Health System Demonstration Projects. 

Funded project teams will receive investigator salary support for 1 year (up to 10% effort for project lead(s) starting July 1, 2025), with an option to extend for a second year, depending upon project progress.  

To aid in the development and improvement of proposals, "office hours" will be offered throughout Phase 1 and 2, for open discussions around possible projects and submissions. Office hours will include a data scientist and a data engineer from the Health AI team to help scope and refine project goals and the APex Enabled Research (AER) team to discuss feasibility. Additionally, during Phase 2 (Open improvement), the UCSF community can comment, follow and like proposals while researchers may refine their proposals.

Read important information about this forum

  • March 3rd - April 6th , 11:59pm: Phase 1 - Open Submission
    • Develop and submit a 3-Page Proposal and a Letter of Support
    • Subscribe to email updates to view new proposals and comments
  • April 7th – April 22nd , 11:59pm: Phase 2 - Open Improvement
    • Browse proposals and comment to improve others' proposals 
    • Review comments on your own proposal and revise if you like 
  • May 15th: Announcement of selected projects
  • July 1st: Funding period begins
Review

Commenting is closed.

Latest Announcement

Review of AI Pilots has been completed

Submitted by Zeanid Noor on May 29, 2025 - 10:59pm Last revised by Zeanid Noor on May 29, 2025 - 11:12pm.

Dear AI Pilot Proposers

 As you know, we’ve had a spectacular response to this RFP and have now completed review of the 40 proposals. Congratulations to the following projects that the review committee selected as winners:

We will be in touch with project leads for next steps.

While we could only award a few projects, we do have several ideas in the works to help the remaining projects / PIs continue to make progress on the exciting and innovative work that has been proposed.  We’ll invite all unselected project teams to an information session to be held this Summer to share these ideas and hear feedback about what might be useful.

This was a highly competitive RFP and we’re encouraged by the breadth of ideas & enthusiasm to bring ‘new knowledge quickly into the healthcare delivery system, including advances in data science, machine learning, and artificial intelligence.’  Thank you for your submission.

AI Pilots Review Committee

 

See all announcements

Proposals (40 total)

Displaying 1 - 10

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Headache Evaluation and Diagnosis - with Generative Artificial INtelligence (HEAD-GAIN): Improving Access

Submitted by Pierre Martin on April 6, 2025 - 11:38am Last revised by Zeanid Noor on May 29, 2025 - 11:16pm.
Proposal Status: 
Awarded

Section 1: The UCSF Health Problem 

GPT-4-DCS: A Large Language Model Pilot to Reduce Hospital Discharge Summary Documentation Burden and to Enhance Quality and Safety

Submitted by Benjamin Rosner on March 28, 2025 - 2:05pm Last revised by Zeanid Noor on May 29, 2025 - 11:15pm.
Proposal Status: 
Awarded

 

Section 1. The UCSF Health Problem 

Leveraging AI for patient-centered post-discharge checklists

Submitted by Seth Blumberg on April 6, 2025 - 11:56pm Last revised by Zeanid Noor on May 29, 2025 - 11:21pm.
Primary Author: Seth Blumberg and Prashant Patel
Proposal Status: 
Review Complete

The UCSF Health problem: Hospital discharges are among the most vulnerable transitions in patient care, where errors and miscommunication can lead to missed follow-up, patient harm, and readmissions. National data has shown that nearly 1-in-5 Medicare patients are readmitted within 30 days of hospital discharge, often due to missed appointments, unfilled prescriptions, or unrecognized clinical deterioration (Jencks et al., NEJM 2009;360:1418-28).

AI-Enhanced Clinical Decision Support Tool to Personalize HIV Treatment and Address Care Delivery Gaps

Submitted by Jose Gutierrez on April 6, 2025 - 11:01pm Last revised by Zeanid Noor on May 29, 2025 - 11:23pm.
Proposal Status: 
Review Complete

Section 1. The UCSF Health Problem. Despite major advancements in antiretroviral therapy (ART), challenges remain for people with HIV (PWH) who are sub-optimally engaged in care due to treatment misalignment, stigma, unstable housing, mental health needs, and other barriers that compromise adherence.1,2 Clinical decisions often follow standardized protocols, overlooking patient preferences and psychosocial factors—typically buried in unstructured electronic healthcare record (EHR) notes.

TPS-Select: An Artificial Intelligence Approach to Guide Transitional Pain Service Referrals for UCSF Neuro-Spine Patients

Submitted by Akash Shanmugam on April 6, 2025 - 10:10pm Last revised by Zeanid Noor on May 29, 2025 - 11:25pm.
Primary Author: Andrew Bishara
Proposal Status: 
Review Complete

Section 1. The UCSF Health Problem

The Whole Person Care Navigator: An AI-Powered Copilot for Social and Behavioral Risk-Informed Care

Submitted by Lucas Zier on April 6, 2025 - 10:00pm Last revised by Zeanid Noor on May 29, 2025 - 11:26pm.
Proposal Status: 
Review Complete

1. The UCSF Health Problem

Enhancing Efficiency and Impact: AI-Powered Eligibility Assessment for Adult Complex Care Management

Submitted by Xinran Liu on April 6, 2025 - 9:48pm Last revised by Zeanid Noor on May 29, 2025 - 11:26pm.
Proposal Status: 
Review Complete
  1. The UCSF Health problem.    

The UCSF Office of Population Health (OPH) Complex Care Management (CCM) team provides advanced care management services to adult patients with high medical and/or psychosocial complexity who are high utilizers of inpatient or emergency department (ED) services. The CCM program involves essential high-touch support such as assessing individual patients’ healthcare challenges, developing targeted care plans, providing health education and coaching, coordinating linkage to care, and connecting to other community resources. Prior analysis of this program’s outcomes showed a statistically significant decrease in ED and observation encounters for patients enrolled in the program. The impact of this program therefore has significant potential to help address UCSF Health’s current ED crowding and bed capacity challenges, reduce readmission rates, and help meet quality metrics associated with specific patient populations. 

Currently, one of the most time-consuming challenges faced by the CCM team is determining individual patient eligibility for the CCM program. Despite using a reporting workbench that identifies patients meeting initial objective criteria, the team must still manually chart review to determine eligibility, which can consume up to 30 minutes per patient. This manual process can also be inconsistent, as it has been noted that different reviewers assess medical and social complexities differently based on their training and clinical background. 

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