Artificial Intelligence (AI) clinical trials at UCLA

2 in progress, 0 open to eligible people

Showing trials for

  • Ambient Artificial Intelligence Scribe Technologies

    Sorry, in progress, not accepting new patients

    This is a three-arm pragmatic RCT of 238 outpatient physicians at a large academic health system, randomized 1:1:1 to one of two AI scribe tools or a usual-care control group. The two-month study will observe and compare the effects of each tool prior to system-wide roll out of selected tool (anticipated Spring 2025). We will use covariate-constrained randomization to balance the arms in terms of physician baseline time in notes, survey-measured level of burnout, and clinic days per week. The primary purpose of the initiative is to improve quality, efficiency, and business operations at University of California, Los Angeles (UCLA) Health, and this initiative is not being done for research purposes. The results of this operational initiative will inform the widespread roll out of AI scribe tools across all providers within the UCLA Health System. Nevertheless, the UCLA study team plans to rigorously examine and publish the impact of this intervention across the health system, which is why the study team pre-registered the initiative.

    Los Angeles, California

  • Artificial Intelligence on Breast Cancer Screening

    Sorry, not yet accepting patients

    The goal of this clinical trial is to compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings and to assess patient's perspectives on AI in medicine. The main questions it aims to answer are: 1. Will AI use be associated with an increase in cancer detection and an initially higher recall rate as radiologists start using AI, followed by a recall rate comparable to that without AI (no more than 1.5 percentage-points higher) after a learning curve period? Will AI use will be associated with lower rates of missed breast cancers and similar rates of false alarms after a learning curve period? 2. Will improved patient outcomes with AI be most pronounced for exams on women who are White, older, and have less dense breasts, and on baseline exams? Will AI aid patient outcomes when the interpretation is by radiologists with less clinical experience, lower annual interpretive volume, and less tolerance of ambiguity? Yet, will there be greater automation bias (the tendency for humans to defer to a computer algorithms' results) noted among these radiologists? 3. What are patients' perspectives on AI in mammography, including their confidence in breast cancer screening when interpreted with vs. without AI? What are patients' perspectives on the importance of the study results? Researchers will compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings. This trial will include all adult patients undergoing 3D mammography breast cancer screening at imaging facilities across University of California at Los Angeles and University of Washington health systems and all radiologists interpreting breast cancer screening. All screening mammograms at these facilities will be randomized to either intervention (radiologist with AI support) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient outcomes.

    Los Angeles, California and other locations

Our lead scientists for Artificial Intelligence (AI) research studies include .

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