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.

Proposals on Review Complete

Proposals (37 total)

Displaying 31 - 37

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TRACE: An AI-Integrated Tool for Early Management in Patients with Pregnancy of Unknown Location

Proposal Status: 

Section 1. The UCSF Health problem

Improving Access to Advance Care Planning Goals of Care Documentation in the EHR Using AI

Proposal Status: 

1. The UCSF Health Problem

AI-Augmented Fall Prevention Tool for Nurses

Proposal Status: 

This proposal outlines the development of an AI-Augmented Fall Prevention Tool aimed at reducing inpatient falls by providing nurses with real-time, tailored, and actionable recommendations using clinical data. The tool integrates a dynamic risk prediction model, LLM-generated clinical decision support system, and feedback mechanism with "a nurse in the loop" to continuously refine model performance. The initiative seeks to improve patient safety, reduce documentation burden, and serve as a scalable model for nursing-led AI innovation in healthcare​.

Harnessing Artificial Intelligence to Develop an Interdisciplinary Approach to Reduce Hospital-Acquired Pressure Injury (HAPI)

Proposal Status: 

The UCSF Health Problem: Hospital Acquired Pressure Injury (HAPI) is a preventable injury to skin or soft tissue that is acquired during a patient’s hospital stay. Reducing HAPI rates is a top priority for UCSF Health leadership as the occurrence of HAPI is detrimental to patient experience and outcomes, results in significant costs (estimated cost to the health system for 1 HAPI is $18,000-$27,000), and is a critical quality measure in the evaluation of hospital performance.

Adaptive closed-loop large language model platform to improve imaging surveillance of intracranial tumors

Proposal Status: 

1. The UCSF Health problem: Imaging surveillance represents a cornerstone of brain tumor management and includes surveillance of incidental lesions that may require future treatment or post-treatment follow-up to ensure disease control. Intracranial lesions are relatively common on magnetic resonance imaging and are incidentally seen in 0.7-1.6% of the general population 1,2. These typically include “benign” tumors that are slow growing and may require years of follow-up.

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