Improving Diagnostic Accuracy with AI Tools
This study is looking at how different ways of showing artificial intelligence (AI) predictions can help doctors and other healthcare professionals make more accurate diagnoses. It's testing two main approaches: one called "Bayesian-Updated Post-Test Probability," which combines a clinician's initial thoughts with AI information, and another called "Standard AI Predicted Probability," which just shows the AI's prediction. The study also looks at whether showing a "95% Confidence Interval" (a range that shows how sure the AI is) helps. The goal is to see if these methods improve diagnostic accuracy for patients with chest pain or shortness of breath. We are looking for 100 healthcare professionals, including Nurse Practitioners, Physician Assistants, and Doctors, who can complete an online survey in English.
- Study design
- This study is an interventional study with 100 participants. It uses a 2x2 factorial within-subjects design, meaning each participant will experience different ways of seeing AI predictions.
- What's involved
- Participants will complete an online survey using a computer or tablet. The primary endpoint, clinician diagnostic accuracy, is measured on Day 1 during survey completion.
- Compensation
- Not stated in the trial record.
- Follow-up
- Participants are followed for diagnostic accuracy, which is measured on Day 1 during survey completion.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Integrating AI Predictions With Clinician Expertise
At a glance
Conditions
NCT07457840
Where you'd take part
This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.
ZSFG
San Francisco, Californiano site contact published
Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.
Study leadership
- Romain Pirracchio, MD, PhD, MPH · PRINCIPAL_INVESTIGATOR · University of California, San Francisco
Who to contact
This trial hasn't published a contact. View it on ClinicalTrials.gov
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Inclusion
Exclusion
What this trial measures
- Clinician Diagnostic AccuracyDay 1 during survey completion
Proportion of correct diagnostic assessments across all vignettes and experimental conditions. For each vignette, participants rate 5 possible diagnoses on a 0-100% probability scale. The diagnosis assigned the highest probability is considered the participant's final diagnosis. Accuracy is determined by comparing the final diagnosis to the ground truth diagnosis established by expert panel consensus (minimum 4 of 5 board-certified physicians in agreement). Analyzed using a generalized linear mixed model (GLMM) with binary outcome (correct vs. incorrect), fixed effects for CLR, uncertainty quantification, misleading AI, and vignette, and a random intercept for participant.