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Artificial Intelligence (AI) in DiagnosisDECIDE-AINCT07027189

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

A study testing Artificial intelligence in diagnosis in people with Artificial Intelligence (AI) in Diagnosis and Artificial Intelligence Supported Image Reviewing, run by Radboud University Medical Center at 1 site in Netherlands.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record
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Trial summary

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. Read more about this study

Who is it for?
Any ageHealthy volunteers can take part
What is being tested?
Artificial intelligence in diagnosis
Could I receive a placebo?
No, everyone receives an active treatment
How many people
25 participants
Where
1 siteNetherlands
Study dates
2 Oct 2025to 2 Jun 2026
Phase
Interventional
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • Healthy volunteers accepted
  • Child, Adult, Older adult

Who may be able to join

  • Graduated, practising dentists.
  • At least three years of experience

Who cannot join

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

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Netherlands1 site

  • Department of Dentistry Radboud Uniersity Medical Center

    Nijmegen, Gelderland, 6525 EX

    Dilek Sezen-Hulsmans, Dentist, 00491708975313, dilek.sezen-hulsmans@radboudumc.nl

    Dr. Cenci, Max.Cenci@radboudumc.nl

Other trials in Netherlands

About this study

Official title: DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study

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.

Read the detailed description

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.

Being tested
Artificial intelligence in diagnosis
Keywords
Artificial Intelligence, Caries Detection, Decision-making, Dental Imaging, Treatment Planning

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Artificial intelligence in diagnosisDiagnostic 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.

Study groups in NCT07027189
Study groupRoleWhat they receive
Phase 1: Caries detection without AI, Phase 2: Caries detection with AIActive comparatorIn 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.
Receives: Diagnostic Test: Artificial intelligence in diagnosis
Phase 1: Caries detection with AI, Phase 2: Caries detection without AIActive comparatorIn 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.
Receives: Diagnostic Test: Artificial intelligence in diagnosis

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Treatment decisions: Compare the treatment recommendations of dentists for caries lesions detected with and without AI support.

    The given options will be "no treatment", "non-invasive treatment" (fluoride varnish, polishing, sealing), and "restoration".

    Participants' answers will be compared to a reference standard.

    Measured: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)

Secondary outcomes (1)

  1. Diagnostic Accuracy in Caries Detection

    RA0: No radiolucency - No visible caries. RA1: Radiolucency confined to the outer half of enamel - Enamel caries. RA2: Radiolucency extending to the inner half of enamel but not reaching dentin - Moderate enamel caries.

    RA3: Radiolucency extending into the outer third of dentin - Dentin caries. RA4: Radiolucency extending into the middle of dentin - Advanced dentin caries. RA5: Radiolucency extending into the inner third of dentin - Severe dentin caries.

    Participants' answers will be compared to a reference standard.

    Measured: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted18 Jun 2025
  2. Study start2 Oct 2025 (estimated)
  3. Primary completion2 Jun 2026 (estimated)
  4. Study completion2 Jun 2026 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Diagnosis
Allocation
Randomised
Intervention model
Crossover
Masking
Single blind
Who is masked
Participant
Enrolment
25 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07027189
Sponsor's protocol ID
2025-18123
Other IDs
2025
Acronym
DECIDE-AI
Status last verified
May 2025
First submitted
28 May 2025
Last updated
18 Jun 2025
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

Publications

  • Panyarak W, Wantanajittikul K, Suttapak W, Charuakkra A, Prapayasatok S. Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS radiographic scoring system. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Feb;135(2):272-281. doi: 10.1016/j.oooo.2022.06.012. Epub 2022 Jul 2.

    PubMed 36513589

  • Ayan E, Bayraktar Y, Celik C, Ayhan B. Dental student application of artificial intelligence technology in detecting proximal caries lesions. J Dent Educ. 2024 Apr;88(4):490-500. doi: 10.1002/jdd.13437. Epub 2024 Jan 10.

    PubMed 38200405

  • Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021 Dec;115:103849. doi: 10.1016/j.jdent.2021.103849. Epub 2021 Oct 14.

    PubMed 34656656

  • Laske M, Opdam NJM, Bronkhorst EM, Braspenning JCC, van der Sanden WJM, Huysmans MCDNJM, Bruers JJ. Minimally Invasive Intervention for Primary Caries Lesions: Are Dentists Implementing This Concept? Caries Res. 2019;53(2):204-216. doi: 10.1159/000490626. Epub 2018 Aug 14.

    PubMed 30107377

  • Ammar N, Kuhnisch J. Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis. Jpn Dent Sci Rev. 2024 Dec;60:128-136. doi: 10.1016/j.jdsr.2024.02.001. Epub 2024 Feb 29.

    PubMed 38450159

  • Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021 Jan;16(1):508-522. doi: 10.1016/j.jds.2020.06.019. Epub 2020 Jun 30.

    PubMed 33384840

Questions about this trial

What is this clinical trial studying?

A study testing Artificial intelligence in diagnosis in people with Artificial Intelligence (AI) in Diagnosis and Artificial Intelligence Supported Image Reviewing, run by Radboud University Medical Center at 1 site in Netherlands.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: opening soon, as of the sponsor's last update on 18 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Treatment decisions: Compare the treatment recommendations of dentists for caries lesions detected with and without AI support. (time frame: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07027189, which also lists 6 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Jun 2025.

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