{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T12:49:25Z","timestamp":1793364565476,"version":"4.5.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Br Dent J"],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:bold>Objective<\/jats:bold>\n                    Artificial intelligence (AI) is reshaping many healthcare disciplines, mainly with newly developed computer systems or machines that have the ability to mimic human intelligence. This paper aims to review the available evidence on the applications of AI in oral medicine. The review critically assesses current evidence, shedding light on AI's growing role in this field.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods<\/jats:bold>\n                    Around 20 applicable studies were included in this review from different databases like PubMed and Google Scholar. Studies included involved original research articles, mini-reviews, systematic reviews and meta-analyses.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results<\/jats:bold>\n                    Existing papers on AI uses in oral medicine included fundamental areas such as oral cancer, lichen planus, bisphosphonate-related osteonecrosis of the jaw, odontogenic keratocysts and oral lesions classification. AI has proved remarkable potential in terms of accuracy, sensitivity and specificity.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion<\/jats:bold>\n                    The outcomes of the papers suggest that AI holds major potential to help dental practitioners diagnose and manage oral diseases with superior precision. While acknowledging the encouraging results, this paper also underscores the necessity for further research and improvement to fully harness the abilities of AI in oral medicine. It calls notice to the fact that AI, although a valued tool, should supplement rather than replace healthcare professionals.\n                  <\/jats:p>","DOI":"10.1038\/s41415-024-8029-9","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T18:03:13Z","timestamp":1732212193000},"page":"765-770","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["AI in oral medicine: is the future already here? A literature review"],"prefix":"10.1038","volume":"237","author":[{"given":"Sultan","family":"Alotaibi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni","family":"Deligianni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"8029_CR5809","unstructured":"Russel S, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. New Jersey: Prentice Hall, 2020."},{"key":"8029_CR5811","unstructured":"Chen Y-W, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020; 51: 248-257."},{"key":"8029_CR5814","unstructured":"Bindushree V, Sameen R J, Vasudevan V, Shrihari T G, Devaraju D, Mathew N S. Artificial intelligence in modern dentistry. J Dent Res Rev 2020; 7: 27-31."},{"key":"8029_CR5816","doi-asserted-by":"crossref","unstructured":"Jubair F, Al-Karadsheh O, Malamos D, Al-Mahdi S, Saad Y, Hassona Y. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 2021; 28: 1123-1130.","DOI":"10.1111\/odi.13825"},{"key":"8029_CR5818","doi-asserted-by":"crossref","unstructured":"Naik N, Hameed B M Z, Shetty D K et al. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front Surg 2022; 9: 862322.","DOI":"10.3389\/fsurg.2022.862322"},{"key":"8029_CR5820","doi-asserted-by":"crossref","unstructured":"Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res 2020; 99: 769-774.","DOI":"10.1177\/0022034520915714"},{"key":"8029_CR5824","doi-asserted-by":"crossref","unstructured":"Shan T, Tay F R, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res 2021; 100: 232-244.","DOI":"10.1177\/0022034520969115"},{"key":"8029_CR5826","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Pola M, Pons-Fuster E, Su\u00e1rez-Fern\u00e1ndez C, Seoane-Romero J, Romero-M\u00e9ndez A, L\u00f3pez-Jornet P. Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review. Cancers (Basel) 2021; 13: 4600.","DOI":"10.3390\/cancers13184600"},{"key":"8029_CR5828","doi-asserted-by":"crossref","unstructured":"Mahmood H, Shaban M, Rajpoot N, Khurram S A. Artificial intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer 2021; 124: 1934-1940.","DOI":"10.1038\/s41416-021-01386-x"},{"key":"8029_CR5830","doi-asserted-by":"crossref","unstructured":"Dave M, Patel N. Artificial intelligence in healthcare and education. Br Dent J 2023; 234: 761-764.","DOI":"10.1038\/s41415-023-5845-2"},{"key":"8029_CR5833","doi-asserted-by":"crossref","unstructured":"Bornstein M M. Artificial intelligence and personalised dental medicine - just a hype or true game changers? Br Dent J 2023; 234: 755.","DOI":"10.1038\/s41415-023-5815-8"},{"key":"8029_CR5835","doi-asserted-by":"crossref","unstructured":"Kar A, Wreesmann V B, Shwetha V et al. Improvement of oral cancer screening quality and reach: the promise of artificial intelligence. J Oral Pathol Med 2020; 49: 727-730.","DOI":"10.1111\/jop.13013"},{"key":"8029_CR5837","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1016\/j.oraloncology.2021.105254"},{"key":"8029_CR5839","doi-asserted-by":"crossref","unstructured":"Kim J-S, Kim B G, Hwang S H. Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14: 3499.","DOI":"10.3390\/cancers14143499"},{"key":"8029_CR5842","doi-asserted-by":"crossref","unstructured":"Patil S, Albogami S, Hosmani J et al. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel) 2022; 12: 1029.","DOI":"10.3390\/diagnostics12051029"},{"key":"8029_CR5844","doi-asserted-by":"crossref","unstructured":"Kudo Y. Predicting cancer outcome: artificial intelligence vs pathologists. Oral Dis 2019; 25: 643-645.","DOI":"10.1111\/odi.12954"},{"key":"8029_CR5846","doi-asserted-by":"crossref","unstructured":"Elmakaty I, Elmarasi M, Amarah A, Abdo R, Malki M I. Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis. Crit Rev Oncol Hematol 2022; 178: 103777.","DOI":"10.1016\/j.critrevonc.2022.103777"},{"key":"8029_CR5848","doi-asserted-by":"crossref","unstructured":"Alabi R O, Youssef O, Pirinen M et al. Machine learning in oral squamous cell carcinoma: current status, clinical concerns and prospects for future - a systematic review. Artif Intell Med 2021; 115: 102060.","DOI":"10.1016\/j.artmed.2021.102060"},{"key":"8029_CR5851","doi-asserted-by":"crossref","unstructured":"Hegde S, Ajila V, Zhu W, Zeng C. Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Pac J Oncol Nurs 2022; 9: 100133.","DOI":"10.1016\/j.apjon.2022.100133"},{"key":"8029_CR5853","doi-asserted-by":"crossref","unstructured":"Al-Rawi N, Sultan A, Rajai B et al. The Effectiveness of Artificial Intelligence in Detection of Oral Cancer. Int Dent J 2022; 72: 436-447.","DOI":"10.1016\/j.identj.2022.03.001"},{"key":"8029_CR5856","doi-asserted-by":"crossref","unstructured":"Gomes R F T, Schuch L F, Martins M D et al. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine - A Systematic Review. J Digit Imaging 2023; 36: 1060-1070.","DOI":"10.1007\/s10278-023-00775-3"},{"key":"8029_CR5859","doi-asserted-by":"crossref","unstructured":"Mahmood H, Shaban M, Indave B I, Santos-Silva A R, Rajpoot N, Khurram S A. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: a systematic review. Oral Oncol 2020; 110: 104885.","DOI":"10.1016\/j.oraloncology.2020.104885"},{"key":"8029_CR5861","doi-asserted-by":"crossref","unstructured":"Rajendran S, Lim J H, Yogalingam K et al. Image collection and annotation platforms to establish a multi-source database of oral lesions. Oral Dis 2023; 29: 2230-2238.","DOI":"10.1111\/odi.14206"},{"key":"8029_CR5863","doi-asserted-by":"crossref","unstructured":"Achararit P, Manaspon C, Jongwannasiri C, Phattarataratip E, Osathanon T, Sappayatosok K. Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks. Eur J Dent 2023; 17: 1275-1282.","DOI":"10.1055\/s-0042-1760300"},{"key":"8029_CR5865","doi-asserted-by":"crossref","unstructured":"Chegani J D, Asadoorian M, Sedghizadeh P P. Artificial Intelligence for Rapid Clinical Diagnosis in Oral Medicine. Oral Surg Oral Med Oral Pathol Oral Radiol 2023; 135: 40-41","DOI":"10.1016\/j.oooo.2022.09.023"},{"key":"8029_CR5868","doi-asserted-by":"crossref","unstructured":"Kim D W, Kim H, Nam W, Kim H J, Cha I-H. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone 2018; 116: 207-214.","DOI":"10.1016\/j.bone.2018.04.020"},{"key":"8029_CR5870","doi-asserted-by":"crossref","unstructured":"Rao R S, Shivanna D B, Mahadevpur K S et al. Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies. Diagnostics (Basel) 2021; 11: 2184.","DOI":"10.3390\/diagnostics11122184"},{"key":"8029_CR5872","doi-asserted-by":"crossref","unstructured":"Gomes R F T, Schmith J, Figueiredo R M de et al. Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images. Int J Environ Res Public Health 2023; 20: 3894.","DOI":"10.3390\/ijerph20053894"}],"container-title":["British Dental Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41415-024-8029-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41415-024-8029-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41415-024-8029-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T19:05:32Z","timestamp":1732215932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41415-024-8029-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":28,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,11,22]]}},"alternative-id":["8029"],"URL":"https:\/\/doi.org\/10.1038\/s41415-024-8029-9","relation":{},"ISSN":["0007-0610","1476-5373"],"issn-type":[{"value":"0007-0610","type":"print"},{"value":"1476-5373","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"18 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declaration"}},{"value":"This study was conducted following all applicable ethical guidelines and regulations. Ethical approval was not sought for this study as it involved a literature review and analysis of existing publicly available data. The information and data used in this study were obtained from previously published academic literature, which is freely accessible and does not involve direct interaction with human subjects or animals. Furthermore, this study did not involve any experiments, clinical trials, or interventions on human participants. It focused solely on synthesising and analysing existing information to review the current state of AI applications in oral medicine. Therefore, no ethical approval was deemed necessary.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declaration"}}]}}