The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

25 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-10-02

Study Completion Date

2026-06-02

Brief Summary

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This study aims to evaluate the influence of artificial intelligence (AI) on the decision-making process for intervention after caries lesion detection. Participants will be dentists working in the Netherlands randomly divided into two groups. Dentists will be divided into two groups and receive a set of bitewing radiographs, which first will be evaluated with or without AI support according to their group. Participants will examine caries lesions on the radiographs and formulate treatment plans accordingly. Then, after a wash-out period of one month, the same radiographs, but in the opposite condition of AI support and again formulate treatment suggestions according to the present caries lesions.

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Detailed Description

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This crossover randomized controlled trial evaluates the effect of artificial intelligence (AI) decision support on dentists' treatment planning following caries detection bitewing radiographs. The study targets clinical decision-making processes by assessing how AI influences diagnostic interpretation and subsequent treatment suggestions. Dentists will be randomly assigned into two study arms. Each participant will evaluate a standardized set of digital bitewing radiographs under two conditions: once with AI assistance and once without, separated by a one-month wash-out period to minimize recall bias. The AI tool provides caries detection prompts based on radiographic analysis but does not suggest treatment. The crossover design enables within-subject comparison, controlling for individual diagnostic thresholds. The radiographs remain constant across both phases to isolate the influence of AI support. The study focuses on diagnostic performance and clinical decision outcomes, both with and without AI support. Treatment decisions are categorized into three predefined levels: no treatment, non-invasive treatment (e.g., fluoride application, polishing, sealing), and invasive intervention (i.e., restorative treatment). Diagnostic accuracy is measured against a reference standard and reported in terms of sensitivity and specificity. Caries detection will be classified using a modified International Caries Classification and Management System (ICCMS). This study design allows to quantify AI's impact on diagnostic performance, as well as on potential shifts in treatment approach. The study aims to contribute to evidence-based guidance on the integration of AI tools into clinical dental practice.

Conditions

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Artificial Intelligence (AI) in Diagnosis Artificial Intelligence Supported Image Reviewing

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Participants

Study Groups

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Phase 1: Caries detection without AI, Phase 2: Caries detection with AI

In this group participants will examine caries lesions on the radiographs without AI support first. Then, after a wash-out period of one month, all participants will re-examine the same radiographs with AI.

Group Type ACTIVE_COMPARATOR

Artificial intelligence in diagnosis

Intervention Type DIAGNOSTIC_TEST

AI-based diagnostic programs have proved to enhance diagnostic performance, however research on its effects on treatment decisions is scarce. In contrast to other studies focusing on AI's accuracy or the resulting increase in dentists' accuracy, this study aims to investigate the differences in dentists' treatment recommendations when supported by AI versus when they are not during caries detection.

Phase 1: Caries detection with AI, Phase 2: Caries detection without AI

In this group participants will examine caries lesions on the radiographs with AI support first. Then, after a wash-out period of one month, all participants will re-examine the same radiographs without AI.

Group Type ACTIVE_COMPARATOR

Artificial intelligence in diagnosis

Intervention Type DIAGNOSTIC_TEST

AI-based diagnostic programs have proved to enhance diagnostic performance, however research on its effects on treatment decisions is scarce. In contrast to other studies focusing on AI's accuracy or the resulting increase in dentists' accuracy, this study aims to investigate the differences in dentists' treatment recommendations when supported by AI versus when they are not during caries detection.

Interventions

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Artificial intelligence in diagnosis

AI-based diagnostic programs have proved to enhance diagnostic performance, however research on its effects on treatment decisions is scarce. In contrast to other studies focusing on AI's accuracy or the resulting increase in dentists' accuracy, this study aims to investigate the differences in dentists' treatment recommendations when supported by AI versus when they are not during caries detection.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

1. Graduated, practising dentists.
2. At least three years of experience

Exclusion Criteria

1. Retired dentists.
2. Specialized practitioners (e.g., orthodontists and oral surgeons) if their typical practice does not involve routine caries diagnostics and treatment planning.

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes