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

Latest Announcement

Open Improvement deadline extended to April 22nd

Submitted by Zeanid Noor on April 15, 2025 - 4:36pm

Due to the large number of proposals received, we are extending the Open Improvement date from April 18th to Tuesday, April 22nd.  

As a reminder, the Open Improvement phase is what makes UCSF Open Proposals different. Others are invited to review proposals, ask questions, offer support, and give suggestions to make good ideas even better. Proposers can use this feedback to improve original proposals prior to the final deadline, now April 22, 2025. 

If you decide to edit / improve your proposal, please briefly summarize (in bullets) any substantive changes and add them to the end of your original proposal in a separate section titled “Summary of Open Improvement Edits”. This will ensure that reviewers will see your changes, i.e., they may not see any discussion in the “Comments”.

See all announcements

Proposals (40 total)

Displaying 1 - 10

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Leveraging AI for patient-centered post-discharge checklists

Submitted by Seth Blumberg on April 6, 2025 - 11:56pm Last revised by Seth Blumberg on April 19, 2025 - 10:12am.
Primary Author: Seth Blumberg and Prashant Patel
Proposal Status: 
Open

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 Jose Gutierrez on April 6, 2025 - 11:17pm.
Proposal Status: 
Open

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 Akash Shanmugam on April 21, 2025 - 12:58pm.
Primary Author: Andrew Bishara
Proposal Status: 
Open

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 Lucas Zier on April 22, 2025 - 10:31am.
Proposal Status: 
Open

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 Xinran Liu on April 22, 2025 - 9:24am.
Proposal Status: 
Open
  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. 

ProUCare - An AI-Preventive Medicine Extender

Submitted by Therese Chan Tack on April 6, 2025 - 9:10pm Last revised by Therese Chan Tack on April 23, 2025 - 12:15am.
Proposal Status: 
Open

The UCSF Health problem

Patients seek preventive health recommendations from their primary care providers (PCP). Health topics include but are not limited to topics like nutrition, musculoskeletal health, injury prevention, and aging well. These broad topics extend beyond the standard 20-minute annual visit and current APeX-derived healthcare maintenance banner topics. 

GIVersa-Endoscopy: A Large Language Model (LLM) based AI Assistant for Endoscopy Sedation Triage

Submitted by Lakshmipriya Subbaraj on April 6, 2025 - 8:54pm Last revised by Lakshmipriya Subbaraj on April 7, 2025 - 7:35am.
Proposal Status: 
Open

Sedation planning is an essential step in the endoscopy preprocedural workflow. Triage of which patients require higher levels of anesthesia support is critical to maximize patient safety and allocate limited anesthesia resources. Current workflow for sedation triage decisions requires significant manual chart review by administrative staff and clinicians. This project aims to develop “GIVersa-Endoscopy,” a custom LLM-powered assistant to augment the sedation triage process for endoscopic procedures at UCSF Health. Integrated into existing clinical workflows, this AI assistant will streamline decision-making and reduce administrative burdens. While this assistant is initially focused on gastroenterology-specific procedures, the administrative challenges and importance of peri-operative sedation triage are widespread across the health system in many divisions, highlighting the larger potential uses for an AI-based sedation triage assistant.

A Clinical Decision Support Tool for Personalized Tacrolimus Dosing in Solid Organ Transplantation

Submitted by Peyton Crest on April 6, 2025 - 8:45pm Last revised by Peyton Crest on April 22, 2025 - 7:48am.
Proposal Status: 
Open
  1. The UCSF Health problem.

Tacrolimus is a major immunosuppressive drug in solid organ transplantation.1 Due to its narrow therapeutic index, however, tacrolimus administration requires strict monitoring and adjustment to achieve optimal therapeutic dosages. Excessive dosages may increase the risk of nephrotoxicity while insufficient dosages can lead to acute rejection.2 Consequently, transplant patients require lifelong monitoring of tacrolimus trough concentrations.

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