Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F25%3A10499977" target="_blank" >RIV/00064165:_____/25:10499977 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/25:10499977

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZC1dI3R3v8" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZC1dI3R3v8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jdent.2025.105868" target="_blank" >10.1016/j.jdent.2025.105868</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial

Popis výsledku v původním jazyce

Objectives: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists&apos; diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic radiographs. We specifically investigated whether AI support influenced diagnostic performance across different levels of clinical experience. Methods: Thirty dentists with varying levels of experience evaluated 50 panoramic radiographs for the presence or absence of PRs, with and without the aid of AI, using a cross-over design. Diagnostic performance metrics, confidence scores, and clinical decision choices were analyzed. CBCT scans served as the reference standard. Outcomes included sensitivity, specificity, positive and negative predictive values, overall diagnostic accuracy, and area under the ROC and AFROC curves. Statistical analyses were conducted using mixed-effects regression models. Results: AI assistance significantly improved overall diagnostic accuracy (91.6 % unaided vs. 93.3 % AI-aided; p &lt; 0.001), mainly by reducing false positive diagnoses (false positive rate: 4.3 % unaided vs. 2.0 % AI-aided). Sensitivity remained stable (46.0 % unaided vs. 45.8 % AI-aided). Junior dentists showed the greatest improvements in performance and confidence. AI support shifted treatment decisions toward more conservative approaches. Conclusions: AI assistance modestly enhanced dentists&apos; diagnostic accuracy for detecting periapical radiolucencies, primarily by decreasing false positive diagnoses. Junior dentists benefited most from AI support. Integration of AI in diagnostic workflows may reduce overtreatment and enhance diagnostic consistency, especially among less experienced clinicians. Clinical Significance: The integration of AI support in dental diagnostics reduced false positive diagnoses and supported more conservative treatment decisions, particularly benefiting less experienced clinicians. These findings suggest that AI assistance can enhance diagnostic consistency and reduce overtreatment in clinical dental practice.

Název v anglickém jazyce

Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial

Popis výsledku anglicky

Objectives: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists&apos; diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic radiographs. We specifically investigated whether AI support influenced diagnostic performance across different levels of clinical experience. Methods: Thirty dentists with varying levels of experience evaluated 50 panoramic radiographs for the presence or absence of PRs, with and without the aid of AI, using a cross-over design. Diagnostic performance metrics, confidence scores, and clinical decision choices were analyzed. CBCT scans served as the reference standard. Outcomes included sensitivity, specificity, positive and negative predictive values, overall diagnostic accuracy, and area under the ROC and AFROC curves. Statistical analyses were conducted using mixed-effects regression models. Results: AI assistance significantly improved overall diagnostic accuracy (91.6 % unaided vs. 93.3 % AI-aided; p &lt; 0.001), mainly by reducing false positive diagnoses (false positive rate: 4.3 % unaided vs. 2.0 % AI-aided). Sensitivity remained stable (46.0 % unaided vs. 45.8 % AI-aided). Junior dentists showed the greatest improvements in performance and confidence. AI support shifted treatment decisions toward more conservative approaches. Conclusions: AI assistance modestly enhanced dentists&apos; diagnostic accuracy for detecting periapical radiolucencies, primarily by decreasing false positive diagnoses. Junior dentists benefited most from AI support. Integration of AI in diagnostic workflows may reduce overtreatment and enhance diagnostic consistency, especially among less experienced clinicians. Clinical Significance: The integration of AI support in dental diagnostics reduced false positive diagnoses and supported more conservative treatment decisions, particularly benefiting less experienced clinicians. These findings suggest that AI assistance can enhance diagnostic consistency and reduce overtreatment in clinical dental practice.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30208 - Dentistry, oral surgery and medicine

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Dentistry

ISSN

0300-5712

e-ISSN

1879-176X

Svazek periodika

160

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

105868

Kód UT WoS článku

001513364800002

EID výsledku v databázi Scopus

2-s2.0-105007851897