{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T12:04:55Z","timestamp":1790078695581,"version":"4.0.1"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Dent. J.","Dentistry Journal"],"abstract":"<jats:p>Background\/Objectives: Periodontitis, the sixth most prevalent disease worldwide, affects over 740 million people and disproportionately burdens transitional economies. Although artificial intelligence (AI)\u2014including deep learning (DL) and machine learning (ML)\u2014achieves high diagnostic accuracy in research settings, a gap persists between proof-of-concept and real-world deployment, especially where regulation is nascent, as in Kazakhstan. This scoping review maps global evidence on AI for periodontal diagnosis, risk prediction, and monitoring; evaluates governance frameworks; and proposes a contextualised implementation model for emerging health systems. Methods: Following PRISMA-ScR and PRISMA 2020 guidance, PubMed\/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library were searched (January 2015\u2013April 2025), supplemented by regulatory and grey literature; ten additional sources published after the search closure were subsequently identified through citation checking and expert peer review during revision, as a targeted amendment rather than a re-executed database search. Forty sources in total (29 peer-reviewed empirical and review studies plus 11 regulatory, grey literature, and patent documents) met the inclusion criteria and were charted thematically. Results: Two dominant paradigms emerged: image-based DL (convolutional neural networks and Vision Transformers), achieving 73\u201398.6% accuracy for radiographic bone loss detection, and ML-based non-clinical screening using patient-reported data and salivary biomarkers. Digital tools (smart toothbrushes, chatbots, and IoT platforms) form a third domain. Performance dropped consistently on external validation, reflecting data quality and sample size constraints. Regulatory analysis showed convergence of the EU AI Act, U.S. FDA framework, WHO guidance, and Kazakhstan\u2019s AI Development Concept (2024\u20132029) around risk-based classification, transparency, and post-market surveillance. Conclusions: Safe, effective AI integration in periodontology requires a phased approach: national multimodal databases, local clinical validation, certified workflow integration, continuous monitoring, population-level surveillance, and legal governance covering liability and insurance. Kazakhstan\u2019s evolving regulatory and digitalisation strategy may offer a context-specific case for evaluating AI adoption pathways across Central Asia and transitional economies, pending prospective validation.<\/jats:p>","DOI":"10.3390\/dj14090615","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging the Implementation Gap: Artificial Intelligence in Periodontology from Proof-of-Concept to Clinical Governance\u2014A Systematic Scoping Review with Evidence from Kazakhstan"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3552-6233","authenticated-orcid":false,"given":"Yerbol","family":"Ayash","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Astana Medical University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aigul","family":"Ismailova","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Astana Medical University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2213-1373","authenticated-orcid":false,"given":"Kenesh","family":"Dzhusupov","sequence":"additional","affiliation":[{"name":"Department of Public Health, International Higher School of Medicine, Bishkek 720054, Kyrgyzstan"},{"name":"Department of Medicine, Osh State University, Osh 723500, Kyrgyzstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8398-3684","authenticated-orcid":false,"given":"Akerke","family":"Chayakova","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Astana Medical University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5148-2383","authenticated-orcid":false,"given":"Anar","family":"Aidarkhanova","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Astana Medical University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1111\/jcpe.12732","article-title":"Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action","volume":"44","author":"Tonetti","year":"2017","journal-title":"J. Clin. Periodontol."},{"key":"ref_2","unstructured":"World Health Organization (2022). Global Oral Health Status Report: Towards Universal Health Coverage for Oral Health by 2030, WHO. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240061484."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1111\/jcpe.13189","article-title":"Periodontitis and cardiovascular diseases: Consensus report","volume":"47","author":"Sanz","year":"2020","journal-title":"J. Clin. Periodontol."},{"key":"ref_4","first-page":"S173","article-title":"Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions","volume":"89","author":"Papapanou","year":"2018","journal-title":"J. Clin. Periodontol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1177\/0022034520915714","article-title":"Artificial intelligence in dentistry: Chances and challenges","volume":"99","author":"Schwendicke","year":"2020","journal-title":"J. Dent. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Scott, J., Biancardi, A.M., Jones, O., and Andrew, D. (2023). Artificial intelligence in periodontology: A scoping review. Dent. J., 11.","DOI":"10.3390\/dj11020043"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.21037\/jmai-24-241","article-title":"Applications of deep learning in periodontal disease diagnosis and management: A systematic review and critical appraisal","volume":"8","author":"Ferrara","year":"2025","journal-title":"J. Med. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jundaeng, J., Chamchong, R., and Nithikathkul, C. (2024). Artificial intelligence-powered innovations in periodontal diagnosis: A new era in dental healthcare. Front. Med. Technol., 6.","DOI":"10.3389\/fmedt.2024.1469852"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jafer, M., Ibraheem, W., Dawood, T., Abbas, A., Hakami, K., Khurayzi, T., Hakami, A.J., Alqahtani, S., Aldosari, M., and Ageely, K. (2025). The application and performance of artificial intelligence (AI) models in the diagnosis, classification, and prediction of periodontal diseases: A systematic review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15243247"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.prosdent.2022.01.026","article-title":"Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review","volume":"130","author":"Barmak","year":"2023","journal-title":"J. Prosthet. Dent."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","article-title":"Deep learning for the radiographic detection of periodontal bone loss","volume":"9","author":"Krois","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dujic, H., Meyer, O., Hoss, P., W\u00f6lfle, U.C., W\u00fclk, A., Meusburger, T., Meier, L., Gruhn, V., Hesenius, M., and Hickel, R. (2023). Automatized detection of periodontal bone loss on periapical radiographs by vision transformer networks. Diagnostics, 13.","DOI":"10.3390\/diagnostics13233562"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e38640","DOI":"10.2196\/38640","article-title":"Tooth-related disease detection system based on panoramic images and optimization through automation: Development study","volume":"10","author":"Kim","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1016\/j.identj.2025.01.002","article-title":"Identification of gingival inflammation surface image features using intraoral scanning and deep learning","volume":"75","author":"Li","year":"2025","journal-title":"Int. Dent. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102186","DOI":"10.1016\/j.xcrm.2025.102186","article-title":"PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image","volume":"6","author":"Tan","year":"2025","journal-title":"Cell Rep. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102185","DOI":"10.1016\/j.xcrm.2025.102185","article-title":"AI-powered digital probing improves periodontal disease diagnosis","volume":"6","author":"Wu","year":"2025","journal-title":"Cell Rep. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alalharith, D.M., Alharthi, H.M., Alghamdi, W.M., Alsenbel, Y.M., Aslam, N., Khan, I.U., Shahin, S.Y., Diani\u0161kov\u00e1, S., Alhareky, M.S., and Barouch, K.K. (2020). A deep learning-based approach for the detection of early signs of gingivitis in orthodontic patients using faster region-based convolutional neural networks. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17228447"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1111\/jcpe.13692","article-title":"Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis","volume":"49","author":"Bashir","year":"2022","journal-title":"J. Clin. Periodontol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1111\/jcpe.13856","article-title":"Development of a machine learning multiclass screening tool for periodontal health status based on non-clinical parameters and salivary biomarkers","volume":"51","author":"Deng","year":"2024","journal-title":"J. Clin. Periodontol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1111\/jcpe.13351","article-title":"Self-reported bleeding on brushing as a predictor of bleeding on probing: Early observations from an IoT network of intelligent power-driven toothbrushes in supportive periodontal care","volume":"47","author":"Tonetti","year":"2020","journal-title":"J. Clin. Periodontol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1093\/dmfr\/twae070","article-title":"Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: Systematic review employing APPRAISE-AI and meta-analysis","volume":"54","author":"Khubrani","year":"2025","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Umer, F., Adnan, S., and Lal, A. (2024). Research and application of artificial intelligence in dentistry from lower-middle income countries: A scoping review. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-03970-y"},{"key":"#cr-split#-ref_23.1","unstructured":"European Parliament and Council of the European Union (2024). Regulation"},{"key":"#cr-split#-ref_23.2","unstructured":"(EU) 2024\/1689 on Artificial Intelligence (Artificial Intelligence Act). Off. J. Eur. Union, 1689, 1-144. Available online: https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj\/eng."},{"key":"ref_24","unstructured":"US Food and Drug Administration (2026, August 20). Artificial Intelligence and Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD) Action Plan, Available online: https:\/\/www.fda.gov\/media\/145022\/download."},{"key":"ref_25","unstructured":"Adilet (2026, August 20). Resolution of the Government of the Republic of Kazakhstan No. 592 of 24 July 2024 \u2018On the Approval of the Concept for the Development of Artificial Intelligence for 2024\u20132029\u2019. Available online: https:\/\/adilet.zan.kz\/rus\/docs\/P2400000592."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chatzopoulos, G.S., Koidou, V.P., Tsalikis, L., and Kaklamanos, E.G. (2025). Clinical applications of artificial intelligence in periodontology: A scoping review. Medicina, 61.","DOI":"10.3390\/medicina61061066"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1111\/joor.14045","article-title":"Applications of artificial intelligence (AI) for diagnosis of periodontal\/peri-implant diseases: A narrative review","volume":"52","author":"Roy","year":"2025","journal-title":"J. Oral Rehabil."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_30","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2026, September 14). PRISMA 2020 Checklist. Available online: https:\/\/www.prisma-statement.org\/prisma-2020-checklist."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1111\/jcpe.70000","article-title":"The association of periodontal inflammation and systemic health indicators: A machine learning approach","volume":"52","author":"Yan","year":"2025","journal-title":"J. Clin. Periodontol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e20250476","DOI":"10.1590\/1678-7757-2025-0476","article-title":"Oral microbiome patterns of dental caries in Kazakhstani adolescents","volume":"33","author":"Issilbayeva","year":"2025","journal-title":"J. Appl. Oral Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sarakbi, R.M., Rama Varma, S., Annamma, L.M., and Sivaswamy, V. (2025). Implications of artificial intelligence in periodontal treatment maintenance: A scoping review. Front. Oral Health, 6.","DOI":"10.3389\/froh.2025.1561128"},{"key":"ref_34","unstructured":"Omar, M.A.A., Alekseevna, Z.L., Nikolaevn, S.M., Aleksandrovich, B.I., Mikhailovich, G.S., and Nikolaevich, S.E. (2026, August 20). Method For Preventing Inflammatory Periodontal Diseases. Available online: https:\/\/patentscope.wipo.int\/search\/ru\/detail.jsf?docId=RU455611051."},{"key":"ref_35","unstructured":"Dmitrievich, B.P., Sergeevich, E.K., Aleksandrovich, B.S., Anatolevich, S.I., Renatovna, M.A., Vladimirovna, Z.G., Vladimirovich, S.A., and Sergeevich, K.S. (2026, August 20). Intelligent Medical Assistant and Method for Providing a User with Health Status Information. Available online: https:\/\/patentscope.wipo.int\/search\/ru\/detail.jsf?docId=RU467881898."},{"key":"ref_36","unstructured":"Adilet (2026, August 20). Law of the Republic of Kazakhstan No. 230-VIII of 17 November 2025 \u2018On Artificial Intelligence\u2019 (in Force from 18 January 2026). Available online: https:\/\/adilet.zan.kz\/eng\/docs\/Z2500000230."},{"key":"ref_37","unstructured":"National Information Technologies JSC (NIT) (2026, August 20). National Supercomputer (Alem.Cloud), Astana: HPE Cray XD670 Servers with NVIDIA H200 GPUs in a Tier III Data Centre; Computing Resources for Universities, Start-Ups, and Research Centres, with Applications Including Healthcare. Available online: https:\/\/www.nitec.kz\/en\/proekty\/national-supercomputer."},{"key":"ref_38","first-page":"e81662","article-title":"Facial scans in clinical dentistry and related research: A scoping review","volume":"17","author":"Shuto","year":"2025","journal-title":"Cureus"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1111\/jre.12999","article-title":"Diagnostic accuracy of active matrix metalloproteinase-8 point-of-care test for the discrimination of periodontal health status: Comparison of saliva and oral rinse samples","volume":"57","author":"Deng","year":"2022","journal-title":"J. Periodontal. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12","DOI":"10.17116\/medtech20224403112","article-title":"Methodical approach and recommendations for scientific description of creation and validation of machine learning model","volume":"44","author":"Gusev","year":"2022","journal-title":"Med. Technol. Assess. Choice"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e44218","DOI":"10.2196\/44218","article-title":"Barriers and enablers to implementing teledentistry from the perspective of dental health care professionals: Protocol for a systematic quantitative, qualitative, and mixed studies review","volume":"12","author":"Inquimbert","year":"2023","journal-title":"JMIR Res. Protoc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kengne Talla, P., Allison, P., Bussi\u00e8res, A., Giraudeau, N., Komarova, S., Basiren, Q., Bergeron, F., and Emami, E. (2024). Teledentistry for improving access to, and quality of oral health care: A protocol for an overview of systematic reviews and meta-analyses. PLoS ONE, 19.","DOI":"10.2196\/preprints.65211"},{"key":"ref_43","unstructured":"Sagatov, I., Dyusupova, A., Abeuova, B., Zhamaliyeva, L., Tokmurziyeva, G., and Kulmagambetov, I. (2022). Assessment of oral health care needs in Kazakhstan: A cross-sectional survey. BMC Oral Health, 22."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kurt-Bayrakdar, S., Bayrakdar, I.\u015e., Yavuz, M.B., Sali, N., \u00c7elik, \u00d6., K\u00f6se, O., Saylan, B.C.U., Kuleli, B., Jagtap, R., and Orhan, K. (2024). Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: A retrospective study. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-03896-5"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105373","DOI":"10.1016\/j.jdent.2024.105373","article-title":"Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph","volume":"150","author":"Xue","year":"2024","journal-title":"J. Dent."},{"key":"ref_46","unstructured":"World Health Organization (2021). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance, WHO. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240029200."},{"key":"ref_47","unstructured":"UNESCO (2021). Recommendation on the Ethics of Artificial Intelligence, UNESCO. Available online: https:\/\/www.unesco.org\/en\/articles\/recommendation-ethics-artificial-intelligence."},{"key":"ref_48","unstructured":"Adilet (2026, August 20). Order of the Minister of Digital Development, Innovations and Aerospace Industry of the Republic of Kazakhstan No. 199\/H\u049a of 21 June 2023 \u2018On the Approval of the Rules for Determining the List of Personal Data\u2019. Available online: https:\/\/adilet.zan.kz\/rus\/docs\/V2300032889."},{"key":"ref_49","unstructured":"(2026, August 20). Order of the Minister of Healthcare of the Republic of Kazakhstan No. KP \u0414CM-39 of 12 May 2021 \u2018On the Approval of Requirements for Electronic Information Resources for Remote Medical Services\u2019. Zakon.kz. Available online: https:\/\/online.zakon.kz\/Document\/?doc_id=37114916."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4299","DOI":"10.1007\/s00784-021-03990-w","article-title":"Deep learning for cephalometric landmark detection: Systematic review and meta-analysis","volume":"25","author":"Schwendicke","year":"2021","journal-title":"Clin. Oral Investig."}],"container-title":["Dentistry Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-6767\/14\/9\/615\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:23:29Z","timestamp":1790076209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-6767\/14\/9\/615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["dj14090615"],"URL":"https:\/\/doi.org\/10.3390\/dj14090615","relation":{},"ISSN":["2304-6767"],"issn-type":[{"value":"2304-6767","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]}}}