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An AI-Powered Voice-Driven Oral Board Examination Simulator for Radiology Training: A Pilot Feasibility Study
Rohit Reddy1, Nicole Brofman2, Nicholas Ott2
1Department of Interventional Radiology, University of Miami, Miller School of Medicine, Miami, Florida.
Background:
The American Board of Radiology is transitioning radiology certifying examinations to a virtual oral format in 2028, creating need for scalable preparation tools, as traditional mock orals require significant faculty resources.
Purpose:
To develop and evaluate feasibility, usability, and educational value of an AI-powered voice-driven oral board examination simulator.
Methods:
In this IRB-approved single-institution pilot (May 2025-June 2026), we developed RadBoardsAI, a web-based platform with real-time voice interaction (Whisper, GPT-4o, text-to-speech), deterministic examiner constraints, and ABR sample cases. Residents (PGY-2-PGY-5) completed simulated examinations and pre/post surveys.
Results:
Eight completed baselines; four (50%) post-intervention; 75% had no prior mock oral experience. Baseline confidence (2.57 ± 0.98) and preparedness (2.14 ± 0.90) were low, with moderate stress (3.14 ± 0.90). Post-intervention (n = 4) showed improved confidence (3.50; SMD = + 0.95) and preparedness (3.50; SMD = + 1.51), decreased stress (1.50; SMD = -1.83), and mean SUS 70.0 ± 15.1 (Good). Qualitative feedback identified image labeling and case expansion as priorities.
Conclusion:
In this pilot feasibility study, RadBoardsAI demonstrates technical feasibility and acceptability among radiology residents, addressing faculty constraints while providing standardized oral board preparation.