The Impact of Clinical Artificial Intelligence in the Digital Workflow in Dentistry - A Narrative Review
- 1. International Journal of Dental Science and Innovative Research (IJDSIR)
Description
Abstract
Artificial intelligence (AI) is progressively emerging as a significant instrument for enhancing clinical processes due to the quick development of digital technology in dentistry. Digital radiographs, CBCT scans, intraoral scans, and electronic health records all produce vast volumes of complicated data that might be challenging to manually analyze in modern dentistry practices. By evaluating these datasets, AI systems may assist physicians in identifying anomalies, assisting with treatment planning, and directing decision-making. By automating processes like picture segmentation, margin detection, and prosthetic design, such systems could improve diagnosis accuracy, decrease clinician variability, and streamline operations.
Despite its potential, AI incorporation into routine dentistry treatment is still in its infancy due to issues with dataset quality, algorithm transparency, ethical considerations, and clinician acceptability. Research indicates promising improvements in workflow and diagnosis accuracy, but there is still little data on actual results. Clinical decision-making may benefit greatly from AI, but its efficacy requires meticulous validation and cautious integration with current digital procedures.
It is therefore essential to comprehend the possibilities and constraints of AI in dentistry. An overview of recent studies on clinical AI applications in digital workflows is given in this review, which emphasizes how AI may help with diagnosis and treatment planning while also pointing out areas that require more research to guarantee safe and efficient use in everyday practice.
Files
NAVAL.pdf
Files
(634.9 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:863d1942fc5f77e9d839effe27ba52b5
|
634.9 kB | Preview Download |
Additional details
Software
References
- 1. Vaid NR, Hansa I, Bichu Y. Smartphone applications used in orthodontics: A scoping review of scholarly literature. J World Fed Orthod. 2020 Oct;9(3 Suppl):S67–73. 2. Chan RSM, Wong WWS, Zhou TY, Liu Y, Tsang HT, Luk CL, et al. Human–AI collaboration improves adults' oral biomechanical functions: A multicentre self-controlled clinical trial. J Dent. 2024 Nov;150:105354. 3. Ogawa R, Ogura I. AI-based computer-aided diagnosis for panoramic radiographs: Quantitative analysis of mandibular cortical morphology in relation to age and gender. J Stomatol Oral Maxillofac Surg. 2022 Sep;123(4):383–7. 4. Adnan N, Ahmed SMF, Das JK, Aijaz S, Sukhia RH, Hoodbhoy Z, et al. Developing an AI-based application for caries index detection on intraoral photographs. Sci Rep. 2024 Nov 5;14(1):26752. 5. Chau RC, Thu KM, Hsung RT, McGrath C, Lam WY. Self-monitoring of oral health using smartphone selfie powered by artificial intelligence: Implications for preventive dentistry. Oral Health Prev Dent. 2024;22:327–40. 6. Gao J, Zhou H, Liu L. Designing a smartphone application for detection of oral bite force using artificial intelligence. Int Dent J. 2025 Aug;75 (4):100799. 7. Wenzel A. Radiographic modalities for diagnosis of caries in a historical perspective: From film to machine-intelligence supported systems. Dentomaxillofac Radiol. 2021 Jul;50(5):20210010. 8. Tyndall DA, Price JB, Gaalaas L, Spin-Neto R. Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise. J Am Dent Assoc. 2024 May;155(5):364–78. 9. Costa ED, Gaêta-Araujo H, Carneiro JA, Zancan BAG, Baranauskas JA, Macedo AA, et al. Development of a dental digital dataset for research in artificial intelligence: The importance of labeling performed by radiologists. Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Jul;138(1):205–13. 10. Ariji Y, Mori M, Fukuda M, Katsumata A, Ariji E. Automatic visualization of the mandibular canal using deep learning segmentation on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2022 Dec;134(6):749–57. 11. Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2022 Jan;51(1):20210197. 12. Cheng H. Digital workflow for full mouth rehabilitation of natural dentition. Zhonghua Kou Qiang Yi Xue Za Zhi. 2025 Aug;60(8):829–37. 13. Lam M, Hajdarević A, Čirgić E, Sabel N. Validity of digital analysis versus manual analysis on orthodontic casts. J World Fed Orthod. 2024 Oct; 13(5):221–8. 14. Wolfaardt JF, Brecht LE, Taft RM, Grant GT. The future of maxillofacial prosthodontics: The role of advanced digital technology and artificial intelligence. J Prosthet Dent. 2024 Jun;131 (6): 1253.e1–34. 15. Revilla-León M, Kois DE, Zeitler JM, Att W, Kois JC. Digital occlusion technologies: Intraoral scanners, jaw tracking systems, and computerized occlusal analysis devices. J Esthet Restor Dent. 2023 Jul;35(5):735–44. 16. Hassan NA, Kamel AE, Omran AE, Gad MW, Ashraf NM, Ahmed OM, et al. Automated identification of dental implants using artificial intelligence. Eur J Prosthodont Restor Dent. 2024 May;32(2):162–7. 17. Cassiano LBA, da Silva JPC, Martins AA, Barbosa MT, Rodrigues KT, Barbosa ÁRL, et al. Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss. Clin Oral Investig. 2025 Mar;29(4):195. 18. Yeslam HE, Freifrau von Maltzahn N, Nassar HM. Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: A narrative review. PeerJ. 2024 Jul;12:e17793.