{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T07:40:16Z","timestamp":1773992416797,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PT national funds","award":["UIDB\/50006\/2020"],"award-info":[{"award-number":["UIDB\/50006\/2020"]}]},{"name":"PT national funds","award":["UIDP\/50006\/2020"],"award-info":[{"award-number":["UIDP\/50006\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>Generative artificial intelligence (GAI) is poised to transform clinical dentistry by enhancing diagnostic accuracy, personalizing treatment planning, and improving procedural precision. This study integrates logic programming and entropy within knowledge representation and reasoning to generate hypotheses, quantify uncertainty, and support clinical decisions. A six-month longitudinal questionnaire was administered to 127 dentists, of whom 119 provided valid responses across four dimensions: current use and knowledge (CUKD), potential applications (PAD), future perspectives (FPD), and challenges and barriers (CBD). Responses, analyzed with both classical statistics and entropy-based measures, revealed significant differences among dimensions (p &lt; 0.01, \u03b72 = 0.14). CUKD, PAD, and FPD all increased steadily over time (baseline means 2.32, 3.06, and 3.27; rising to 3.75, 4.51, and 4.71, respectively), while CBD remained more variable (1.87\u20133.87). The overall entropic state declined from 0.43 to 0.31 (p = 0.018), reflecting reduced uncertainty. Statistical and entropy-derived trends converged, suggesting growing professional clarity and cautious acceptance of GAI. These findings indicate that, despite persistent concerns, GAI holds promise for advancing adaptive and evidence-driven dental practice.<\/jats:p>","DOI":"10.3390\/digital5030044","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T14:45:46Z","timestamp":1758206746000},"page":"44","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Generative Artificial Intelligence in Clinical Dentistry: Enhancing Diagnosis, Treatment Planning, and Procedural Precision Through Advanced Knowledge Representation and Reasoning"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-8222","authenticated-orcid":false,"given":"Hossam","family":"Dawa","sequence":"first","affiliation":[{"name":"AI & Health\u2014Research Unit in Artificial Intelligence Applied to Health, CESPU University, Rua Jos\u00e9 Ant\u00f3nio Vidal, 81, 4760-409 Famalic\u00e3o, Portugal"},{"name":"UNIPRO\u2014Oral Pathology and Rehabilitation Research Unit, University Institute of Health Science, CESPU, Avenida Central de Gandra, 1317, 4585-116 Gandra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6591-7256","authenticated-orcid":false,"given":"Arthur","family":"Cortes","sequence":"additional","affiliation":[{"name":"Department of Dental Surgery, University of Malta, MSD 2080 Msida, Malta"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2129-6333","authenticated-orcid":false,"given":"Carlos","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"UNIPRO\u2014Oral Pathology and Rehabilitation Research Unit, University Institute of Health Science, CESPU, Avenida Central de Gandra, 1317, 4585-116 Gandra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8863-0351","authenticated-orcid":false,"given":"Jos\u00e9","family":"Neves","sequence":"additional","affiliation":[{"name":"AI & Health\u2014Research Unit in Artificial Intelligence Applied to Health, CESPU University, Rua Jos\u00e9 Ant\u00f3nio Vidal, 81, 4760-409 Famalic\u00e3o, Portugal"},{"name":"LASI\u2014Associated Laboratory of Intelligent Systems, ALGORITMI Research Centre, University of Minho, Campus de Gualtar, Rua da Universidade, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8456-7773","authenticated-orcid":false,"given":"Henrique","family":"Vicente","sequence":"additional","affiliation":[{"name":"LASI\u2014Associated Laboratory of Intelligent Systems, ALGORITMI Research Centre, University of Minho, Campus de Gualtar, Rua da Universidade, 4710-057 Braga, Portugal"},{"name":"Department of Chemistry and Biochemistry, School of Science and Technology, University of \u00c9vora, Rua Rom\u00e3o Ramalho, 59, 7000-671 \u00c9vora, Portugal"},{"name":"LAQV REQUIMTE\u2014Associated Laboratory for Green Chemistry of the Network of Chemistry and Technology, University of \u00c9vora, Rua Rom\u00e3o Ramalho, 59, 7000-671 \u00c9vora, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"ref_1","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. 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