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Currently developed models can support diagnosis, treatment planning, and maintenance monitoring. However, most of the available literature is based on retrospective and often single\u2010modality data sets. Therefore, they lack clinical generalizability and underrepresent the multimodal nature of periodontal and peri\u2010implant diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives and Methods<\/jats:title>\n                    <jats:p>This study examines the current status of AI in the field of periodontology and implant dentistry and discusses the associated ethical and translational challenges. A narrative review search was conducted in PubMed, Embase, and Cochrane Library through February 2026. Furthermore, it explains the responsible implementation methods and proposes a roadmap for the sustainable adoption of AI in this field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The use of AI showed major promise in the field of periodontology and implantology. The main applications of AI in this field include bone loss detection, detection of intrabony defects, biofilm and microbiome\u2010related analysis, patient communication, posttreatment monitoring, implant planning, detection of the implant system, and predicting the implant prognosis. Utilizing AI in these domains improved the efficiency, consistency, and access to care to some extent. However, there are major limitations that need to be addressed to move toward a responsible and sustainable use of AI. These limitations include reliance on single modality and single\u2010center and nonshareable data sets, incomplete integration of clinical and biological variables, limited external validation, and persistent concern regarding bias, privacy, and explainability. These factors frame AI in periodontology as a clinician\u2010supervised, data\u2010dependent, and ethically guided technology rather than an isolated diagnostic tool. One of the most important factors that needs to be included in the training of clinicians is that they need to gain education on critiquing the AI outputs, use it as an assistive tool, and never over\u2010rely on the AI outputs. The current evidence also suggests moving beyond the single\u2010modality AI toward multimodal systems that better reflect the 2018 periodontal classification and the complexity of periodontal and peri\u2010implant decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Use of AI has substantially improved periodontology and implant dentistry research. However, a sustainable clinical translation requires responsible implementation of the AI and a stronger data set that represents the multimodal nature of the periodontal and peri\u2010implant diseases. Moving forward, the data sets being used to train the models need to be multimodal, multicenter, and clinically verified to support the generalizability of the findings. In addition, there needs to be clear ethical safeguards and clinician oversight to make sure the results are interpreted in the correct way.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>As AI is becoming more prevalent among periodontists and other clinicians as an assistive tool, there is a clear need for proper education on how to use AI more effectively and responsibly. AI models and their outputs can be used for strengthening diagnosis, prognosis, treatment planning, and supportive care only when the full clinical context is provided. The long\u2010term value of this integration of AI into clinical periodontology will largely depend on the following: whether it can be validated across diverse populations and whether it is supported by a shared clinical data infrastructure. This will ensure precise and consistent patient care in the real\u2010world application of AI in the clinical setting.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/prd.70078","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T08:55:10Z","timestamp":1788166510000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Guiding the\n                    <scp>AI<\/scp>\n                    revolution in periodontology and implant dentistry: Concepts, ethics, accountability, and a roadmap for sustainable adoption"],"prefix":"10.1111","author":[{"given":"Aminollah","family":"Khormali","sequence":"first","affiliation":[{"name":"Adams School of Dentistry University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Negin","family":"Soghli","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Adams School of Dentistry University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1255-3887","authenticated-orcid":false,"given":"Patricia A.","family":"Miguez","sequence":"additional","affiliation":[{"name":"Department of Periodontics, School of Dental Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdmed.2023.1085251"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-44839-3"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/prd.12586"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-022-02436-3"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/froh.2025.1561128"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2025.106130"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/00220345251356408"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s41073-019-0064-8"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(83)90008-5"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103839"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-64509-z"},{"key":"e_1_2_12_13_1","unstructured":"National Institute of Standards and Technology (NIST).Artificial Intelligence (AI). 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