Exploration of AI-Powered Tools for Risk Assessment in General Dentistry
- PMID: 40655801
- PMCID: PMC12244987
- DOI: 10.4103/jpbs.jpbs_83_25
Exploration of AI-Powered Tools for Risk Assessment in General Dentistry
Abstract
Background: The integration of artificial intelligence (AI) in dentistry has transformed diagnostic accuracy and treatment planning. AI-powered tools have shown promise in enhancing risk assessment, enabling early identification of oral health conditions.
Materials and methods: A prospective study was conducted to evaluate the efficiency of an AI-powered risk assessment tool. A total of 150 patients, aged 18-65 years, were included in the study. Patients underwent standard clinical examinations, followed by AI-based risk assessment using a machine learning platform trained on a dataset of 10,000 cases. The tool analyzed factors, such as oral hygiene habits, dietary patterns, and medical history, to generate individualized risk scores. Statistical analysis compared AI-generated risk assessments with those of dental experts to measure accuracy and reliability.
Results: The AI tool demonstrated a sensitivity of 91% and a specificity of 88% in identifying high-risk cases. Of the 150 patients, 45 were identified as high risk, 70 as moderate risk, and 35 as low risk by the AI tool. Expert evaluation aligned with AI predictions in 92% of cases, confirming the tool's reliability. Time required for risk assessment was reduced by 40% compared to manual evaluations.
Conclusion: AI-powered tools offer significant advantages in general dentistry by improving the accuracy and efficiency of risk assessment. These tools can serve as valuable adjuncts to clinical expertise, enabling early interventions and personalized care strategies.
Keywords: Artificial intelligence; diagnostic tools; general dentistry; machine learning; oral health; risk assessment.
Copyright: © 2025 Journal of Pharmacy and Bioallied Sciences.
Conflict of interest statement
There are no conflicts of interest.
Similar articles
-
Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.Clin Orthop Relat Res. 2024 Dec 1;482(12):2193-2208. doi: 10.1097/CORR.0000000000003185. Epub 2024 Jul 23. Clin Orthop Relat Res. 2024. PMID: 39051924
-
Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.Cochrane Database Syst Rev. 2022 May 20;5(5):CD013665. doi: 10.1002/14651858.CD013665.pub3. Cochrane Database Syst Rev. 2022. PMID: 35593186 Free PMC article.
-
Artificial intelligence for diagnosing exudative age-related macular degeneration.Cochrane Database Syst Rev. 2024 Oct 17;10(10):CD015522. doi: 10.1002/14651858.CD015522.pub2. Cochrane Database Syst Rev. 2024. PMID: 39417312
-
Screening for aspiration risk associated with dysphagia in acute stroke.Cochrane Database Syst Rev. 2021 Oct 18;10(10):CD012679. doi: 10.1002/14651858.CD012679.pub2. Cochrane Database Syst Rev. 2021. PMID: 34661279 Free PMC article.
-
Artificial intelligence for detecting keratoconus.Cochrane Database Syst Rev. 2023 Nov 15;11(11):CD014911. doi: 10.1002/14651858.CD014911.pub2. Cochrane Database Syst Rev. 2023. PMID: 37965960 Free PMC article.
References
-
- Lee JH, Kim DH, Jeong SN, Choi SH. Application of artificial intelligence in dentistry. J Dent Res. 2020;99:768–74.
-
- Carvalho BKG, Nolden EL, Wenning AS, Kiss-Dala S, Agócs G, Róth I, et al. Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis. J Dent. 2024;151:105388. - PubMed
-
- Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25:24–9. - PubMed
-
- Mupparapu M, Wu CW. Artificial intelligence, machine learning, neural networks, and deep learning: Future of dentistry. J Indian Prosthodont Soc. 2020;20:240–8.
LinkOut - more resources
Full Text Sources