Transforming Clinical Decision Support Systems: Using Continuous Bayesian Updates to Integrate AI Predictions With Clinician Expertise
Trial ID: NCT07457840
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2026-12
Sponsor: University of California, San Francisco
Conditions: Diagnostic Decision Making
Interventions: Bayesian-Updated Post-Test Probability; Standard AI Predicted Probability; Uncertainty Quantification (95% Confidence Interval)
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Optimizing the interaction between the human and the machine is a major topic when deploying artificial intelligence (AI) at the bedside. The goal of this randomized clinical vignette study is to learn if presenting AI model outputs via continuous Bayesian updates and/or uncertainty quantification can improve diagnostic accuracy and clinician trust in healthcare professionals (physicians, residents, fellows, physician assistants (PAs), and nurse practitioners (NPs)) from US academic institutions evaluating patients with chest pain or dyspnea.

The main questions it aims to answer are:

* Does presenting AI predictions as Bayesian-updated post-test probabilities improve diagnostic accuracy compared to standard predicted probabilities?
* Does the addition of uncertainty quantification (95% confidence intervals) to AI predictions improve diagnostic accuracy?
* Do these interventions (Bayesian updating and/or uncertainty quantification) help clinicians recover from the negative effects of intentionally misleading AI predictions?

Comparison: Researchers will compare standard AI predicted probabilities (presented without uncertainty) to Bayesian-updated post-test probabilities and/or outputs containing 95% confidence intervals to see if the interventions improve diagnostic accuracy, clinician confidence, and resilience against misleading AI.

Participants will:

* Review 8 clinical vignettes (simulated patient cases) focusing on chest pain or dyspnea.
* Provide an initial "pre-test" diagnostic probability for 5 possible diagnoses based on the clinical history alone.
* View AI model outputs that vary by experimental condition (standard probability vs. Bayesian update, with or without uncertainty intervals, and accurate vs. misleading).
* Provide an updated "post-test" diagnostic probability for the diagnoses after viewing the AI output.
* Select and rank diagnostic tests and therapeutic steps for each vignette. Complete a post-survey regarding their trust in the AI, comfort with the data presentation, and demographics.

Eligibility criteria:
Inclusion Criteria:

* Must hold one of the following clinical roles: Nurse Practitioner (NP), Physician Assistant/Physician Associate (PA), Resident Physician, Physician Fellow, or Attending Physician
* Able to complete the survey in English
* Access to a computer or tablet (mobile phones are not recommended due to the visual nature of the survey)

Exclusion Criteria:

* Does not hold an eligible clinical role as defined above
* Completes fewer than 2 of 8 clinical vignettes (less than 25% of the survey)
* Has previously participated in this study
* Unable to complete the survey in English

Outcomes:
Clinician Diagnostic Accuracy