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.
Age range
18 Years
Sex
ALL
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Clinician Diagnostic Accuracy
Timeframe: Day 1 during survey completion