TY - JOUR

T1 - AI-initiated second opinions

T2 - a framework for advanced caries treatment planning

AU - Dascalu, Tudor

AU - Ramezanzade, Shaqayeq

AU - Bakhshandeh, Azam

AU - Bjørndal, Lars

AU - Ibragimov, Bulat

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024

Y1 - 2024

N2 - Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians’ distrust of computer predictions and the potential risks associated with erroneous outputs. We introduce the idea of using AI to trigger second opinions in cases where there is a disagreement between the clinician and the algorithm. By keeping the AI prediction hidden throughout the diagnostic process, we minimize the risks associated with distrust and erroneous predictions, relying solely on human predictions. The experiment involved 3 experienced dentists, 25 dental students, and 290 patients treated for advanced caries across 6 centers. We developed an AI model to predict pulp status following advanced caries treatment. Clinicians were asked to perform the same prediction without the assistance of the AI model. The second opinion framework was tested in a 1000-trial simulation. The average F1-score of the clinicians increased significantly from 0.586 to 0.645.

AB - Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians’ distrust of computer predictions and the potential risks associated with erroneous outputs. We introduce the idea of using AI to trigger second opinions in cases where there is a disagreement between the clinician and the algorithm. By keeping the AI prediction hidden throughout the diagnostic process, we minimize the risks associated with distrust and erroneous predictions, relying solely on human predictions. The experiment involved 3 experienced dentists, 25 dental students, and 290 patients treated for advanced caries across 6 centers. We developed an AI model to predict pulp status following advanced caries treatment. Clinicians were asked to perform the same prediction without the assistance of the AI model. The second opinion framework was tested in a 1000-trial simulation. The average F1-score of the clinicians increased significantly from 0.586 to 0.645.

KW - Artificial intelligence

KW - CAD

KW - Caries

KW - Computer vision/convolutional neural networks

U2 - 10.1186/s12903-024-04551-9

DO - 10.1186/s12903-024-04551-9

M3 - Journal article

C2 - 38987714

AN - SCOPUS:85198120319

VL - 24

JO - BMC Oral Health

JF - BMC Oral Health

SN - 1472-6831

IS - 1

M1 - 772

ER -