AI-initiated second opinions: a framework for advanced caries treatment planning.

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.

Prediction of pulp exposure before caries excavation using artificial intelligence: Deep learning-based image data versus standard dental radiographs.

OBJECTIVES: The objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability. METHODS: 292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths merged and were followed by fully connected layers that predicted the probability of pulp exposure. A trial concerning the prediction of pulp exposure based on radiographic input and information on age and pain was conducted, involving 25 dental students. The data displayed was divided into 4 groups (G): GX-ray, GX-ray+clinical data, GX-ray+AI, GX-ray+clinical data+AI. RESULTS: The results showed that AI surpassed the performance of students in all groups with an F1-score of 0.71 (P < 0.001). The students' F1-score in GX-ray+AI and GX-ray+clinical data+AI with model prediction (0.61 and 0.61 respectively) was slightly higher than the F1-score in GX-ray and GX-ray+clinical data (0.58 and 0.59 respectively) with a borderline statistical significance of P = 0.054. CONCLUSIONS: Although the AI model had much better performance than all groups, the participants when given AI prediction, benefited only 'slightly'. AI technology seems promising, but more explainable AI predictions along with a 'learning curve' are warranted.