Skip to main content
Top
Published in: Head & Face Medicine 1/2023

Open Access 01-12-2023 | Artificial Intelligence | Research

Patients’ perspectives on the use of artificial intelligence in dentistry: a regional survey

Authors: Nasim Ayad, Falk Schwendicke, Joachim Krois, Stefanie van den Bosch, Stefaan Bergé, Lauren Bohner, Marcel Hanisch, Shankeeth Vinayahalingam

Published in: Head & Face Medicine | Issue 1/2023

Login to get access

Abstract

The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients’ perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher’s exact tests with Monte Carlo approximation. Patients’ perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor–patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1–5 (42.3%) or 5–10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18–35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients’ perceptions may allow professionals to shape AI-driven dentistry in the future.
Literature
1.
go back to reference Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: a scoping review. J Dent. 2019;91: 103226.PubMedCrossRef Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: a scoping review. J Dent. 2019;91: 103226.PubMedCrossRef
2.
go back to reference Mörch CM, Atsu S, Cai W, Li X, Madathil SA, Liu X, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100:1452–60.PubMedCrossRef Mörch CM, Atsu S, Cai W, Li X, Madathil SA, Liu X, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100:1452–60.PubMedCrossRef
3.
go back to reference Vinayahalingam S, Goey R, Kempers S, Schoep J, Cherici T, Moin DA, et al. Automated chart filing on panoramic radiographs using deep learning. J Dent. 2021;115: 103864.PubMedCrossRef Vinayahalingam S, Goey R, Kempers S, Schoep J, Cherici T, Moin DA, et al. Automated chart filing on panoramic radiographs using deep learning. J Dent. 2021;115: 103864.PubMedCrossRef
4.
go back to reference Choi E, Lee S, Jeong E, Shin S, Park H, Youm S, et al. Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography. Sci Rep. 2022;12:2456.PubMedPubMedCentralCrossRef Choi E, Lee S, Jeong E, Shin S, Park H, Youm S, et al. Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography. Sci Rep. 2022;12:2456.PubMedPubMedCentralCrossRef
5.
go back to reference Lee S, Kim D, Jeong H-G. Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence. Sci Rep. 2022;12:5172.PubMedPubMedCentralCrossRef Lee S, Kim D, Jeong H-G. Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence. Sci Rep. 2022;12:5172.PubMedPubMedCentralCrossRef
6.
go back to reference Angelis FD, Pranno N, Franchina A, Carlo SD, Brauner E, Ferri A, et al. Artificial intelligence: a new diagnostic software in dentistry: a preliminary performance diagnostic study. Int J Environ Res Public Health. 2022;19:1728.PubMedPubMedCentralCrossRef Angelis FD, Pranno N, Franchina A, Carlo SD, Brauner E, Ferri A, et al. Artificial intelligence: a new diagnostic software in dentistry: a preliminary performance diagnostic study. Int J Environ Res Public Health. 2022;19:1728.PubMedPubMedCentralCrossRef
7.
8.
go back to reference Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: randomized trial. J Dent. 2021;115: 103849.PubMedCrossRef Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: randomized trial. J Dent. 2021;115: 103849.PubMedCrossRef
9.
go back to reference Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. WIREs Data Min Knowl Discov. 2019;9:1312. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. WIREs Data Min Knowl Discov. 2019;9:1312.
10.
go back to reference Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99:769–74.PubMedCrossRef Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99:769–74.PubMedCrossRef
11.
go back to reference Gerke S, Babic B, Evgeniou T, Cohen IG. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med. 2020;3:53.PubMedPubMedCentralCrossRef Gerke S, Babic B, Evgeniou T, Cohen IG. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med. 2020;3:53.PubMedPubMedCentralCrossRef
12.
go back to reference Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11:5193.PubMedPubMedCentralCrossRef Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11:5193.PubMedPubMedCentralCrossRef
13.
go back to reference Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: a survey. Imaging Sci Dent. 2020;50:193–8.PubMedPubMedCentralCrossRef Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: a survey. Imaging Sci Dent. 2020;50:193–8.PubMedPubMedCentralCrossRef
14.
go back to reference Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021;85:60–8.PubMedCrossRef Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021;85:60–8.PubMedCrossRef
15.
go back to reference Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol. 2021;50:20200461.PubMedPubMedCentralCrossRef Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol. 2021;50:20200461.PubMedPubMedCentralCrossRef
16.
go back to reference Kosan E, Krois J, Wingenfeld K, Deuter CE, Gaudin R, Schwendicke F. Patients’ perspectives on artificial intelligence in dentistry: a controlled study. J Clin Med. 2022;11:2143.PubMedPubMedCentralCrossRef Kosan E, Krois J, Wingenfeld K, Deuter CE, Gaudin R, Schwendicke F. Patients’ perspectives on artificial intelligence in dentistry: a controlled study. J Clin Med. 2022;11:2143.PubMedPubMedCentralCrossRef
17.
go back to reference Haan M, Ongena YP, Hommes S, Kwee TC, Yakar D. A qualitative study to understand patient perspective on the use of artificial intelligence in radiology. J Am Coll Radiol. 2019;16:1416–9.PubMedCrossRef Haan M, Ongena YP, Hommes S, Kwee TC, Yakar D. A qualitative study to understand patient perspective on the use of artificial intelligence in radiology. J Am Coll Radiol. 2019;16:1416–9.PubMedCrossRef
18.
go back to reference Chen Y-W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51:248–57.PubMed Chen Y-W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51:248–57.PubMed
20.
go back to reference Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol. 2016;13:1415–20.PubMedCrossRef Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol. 2016;13:1415–20.PubMedCrossRef
21.
go back to reference Pakdemirli E. Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading? Acta Radiologica Open. 2019;8:205846011983022.CrossRef Pakdemirli E. Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading? Acta Radiologica Open. 2019;8:205846011983022.CrossRef
22.
go back to reference Pethani F. Promises and perils of artificial intelligence in dentistry. Aust Dent J. 2021;66:124–35.PubMedCrossRef Pethani F. Promises and perils of artificial intelligence in dentistry. Aust Dent J. 2021;66:124–35.PubMedCrossRef
23.
go back to reference Bisdas S, Topriceanu C-C, Zakrzewska Z, Irimia A-V, Shakallis L, Subhash J, et al. Artificial intelligence in medicine: a multinational multi-center survey on the medical and dental students’ perception. Front Public Health. 2021;9: 795284.PubMedPubMedCentralCrossRef Bisdas S, Topriceanu C-C, Zakrzewska Z, Irimia A-V, Shakallis L, Subhash J, et al. Artificial intelligence in medicine: a multinational multi-center survey on the medical and dental students’ perception. Front Public Health. 2021;9: 795284.PubMedPubMedCentralCrossRef
24.
go back to reference Ongena YP, Haan M, Yakar D, Kwee TC. Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire. Eur Radiol. 2020;30:1033–40.PubMedCrossRef Ongena YP, Haan M, Yakar D, Kwee TC. Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire. Eur Radiol. 2020;30:1033–40.PubMedCrossRef
25.
go back to reference Promberger M, Baron J. Do patients trust computers? J Behav Decis Making. 2006;19:455–68.CrossRef Promberger M, Baron J. Do patients trust computers? J Behav Decis Making. 2006;19:455–68.CrossRef
26.
go back to reference Önkal D, Goodwin P, Thomson M, Gönül S, Pollock A. The relative influence of advice from human experts and statistical methods on forecast adjustments. J Behav Decis Making. 2009;22:390–409.CrossRef Önkal D, Goodwin P, Thomson M, Gönül S, Pollock A. The relative influence of advice from human experts and statistical methods on forecast adjustments. J Behav Decis Making. 2009;22:390–409.CrossRef
27.
go back to reference Recht M, Bryan RN. Artificial intelligence: threat or boon to radiologists? J Am Coll Radiol. 2017;14:1476–80.PubMedCrossRef Recht M, Bryan RN. Artificial intelligence: threat or boon to radiologists? J Am Coll Radiol. 2017;14:1476–80.PubMedCrossRef
28.
go back to reference Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J Dent Res. 2021;100:232–44.PubMedCrossRef Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J Dent Res. 2021;100:232–44.PubMedCrossRef
29.
go back to reference Schwendicke F, Rossi JG, Göstemeyer G, Elhennawy K, Cantu AG, Gaudin R, et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J Dent Res. 2021;100:369–76.PubMedCrossRef Schwendicke F, Rossi JG, Göstemeyer G, Elhennawy K, Cantu AG, Gaudin R, et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J Dent Res. 2021;100:369–76.PubMedCrossRef
30.
go back to reference Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Netw Open. 2022;5: e220269.PubMedPubMedCentralCrossRef Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Netw Open. 2022;5: e220269.PubMedPubMedCentralCrossRef
32.
go back to reference Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106–11.PubMedCrossRef Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106–11.PubMedCrossRef
33.
go back to reference Devlin H, Williams T, Graham J, Ashley M. The ADEPT study: a comparative study of dentists’ ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software. Br Dent J. 2021;231:481–5.PubMedPubMedCentralCrossRef Devlin H, Williams T, Graham J, Ashley M. The ADEPT study: a comparative study of dentists’ ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software. Br Dent J. 2021;231:481–5.PubMedPubMedCentralCrossRef
34.
go back to reference Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod. 2020;46:987–93.PubMedCrossRef Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod. 2020;46:987–93.PubMedCrossRef
35.
go back to reference Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. J Endod. 2021;47:1352–7.PubMedCrossRef Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. J Endod. 2021;47:1352–7.PubMedCrossRef
37.
go back to reference Yilmaz E, Kayikcioglu T, Kayipmaz S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Progr Biomed. 2017;146:91–100.CrossRef Yilmaz E, Kayikcioglu T, Kayipmaz S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Progr Biomed. 2017;146:91–100.CrossRef
38.
go back to reference Ilhan B, Guneri P, Wilder-Smith P. The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer. Oral Oncol. 2021;116: 105254.PubMedPubMedCentralCrossRef Ilhan B, Guneri P, Wilder-Smith P. The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer. Oral Oncol. 2021;116: 105254.PubMedPubMedCentralCrossRef
39.
go back to reference Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. Artificial Intelligence: applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg. 2019;120:347–54.PubMedCrossRef Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. Artificial Intelligence: applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg. 2019;120:347–54.PubMedCrossRef
40.
go back to reference Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J CARS. 2016;11:1297–309.CrossRef Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J CARS. 2016;11:1297–309.CrossRef
41.
go back to reference Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics : evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020;81:52–68.PubMedCrossRef Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics : evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020;81:52–68.PubMedCrossRef
43.
go back to reference Camci H, Salmanpour F. Estimating the size of unerupted teeth: Moyers vs. deep learning. Am J Orthod Dentofac Orthop. 2022;161:451.CrossRef Camci H, Salmanpour F. Estimating the size of unerupted teeth: Moyers vs. deep learning. Am J Orthod Dentofac Orthop. 2022;161:451.CrossRef
44.
go back to reference Monill-González A, Rovira-Calatayud L, d’Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: where are we now? A scoping review. Orthod Craniofac Res. 2021;24(Suppl 2):6–15.PubMedCrossRef Monill-González A, Rovira-Calatayud L, d’Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: where are we now? A scoping review. Orthod Craniofac Res. 2021;24(Suppl 2):6–15.PubMedCrossRef
45.
go back to reference Kılıc MC, Bayrakdar IS, Çelik Ö, Bilgir E, Orhan K, Aydın OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50:20200172.PubMedPubMedCentralCrossRef Kılıc MC, Bayrakdar IS, Çelik Ö, Bilgir E, Orhan K, Aydın OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50:20200172.PubMedPubMedCentralCrossRef
46.
go back to reference Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol. 2020;49:20190107.PubMedCrossRef Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol. 2020;49:20190107.PubMedCrossRef
47.
go back to reference Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform. 2019;28:128–34.PubMedPubMedCentralCrossRef Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform. 2019;28:128–34.PubMedPubMedCentralCrossRef
48.
go back to reference Yasa Y, Çelik Ö, Bayrakdar IS, Pekince A, Orhan K, Akarsu S, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontol Scand. 2021;79:275–81.PubMedCrossRef Yasa Y, Çelik Ö, Bayrakdar IS, Pekince A, Orhan K, Akarsu S, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontol Scand. 2021;79:275–81.PubMedCrossRef
Metadata
Title
Patients’ perspectives on the use of artificial intelligence in dentistry: a regional survey
Authors
Nasim Ayad
Falk Schwendicke
Joachim Krois
Stefanie van den Bosch
Stefaan Bergé
Lauren Bohner
Marcel Hanisch
Shankeeth Vinayahalingam
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Head & Face Medicine / Issue 1/2023
Electronic ISSN: 1746-160X
DOI
https://doi.org/10.1186/s13005-023-00368-z

Other articles of this Issue 1/2023

Head & Face Medicine 1/2023 Go to the issue