Artificial Intelligence and the Evolving Standard of Care in Dentistry: Clinical Evidence, Regulatory Landscape, and Medicolegal Implications.

Authors

Ronald D Perry, Driss Zoukhri, Volodymyr Kachmar, Enxhi Subashi

Published in

Compendium of continuing education in dentistry (Jamesburg, N.J. : 1995). Volume 47. Issue 6. Pages 268-275.

Abstract

Artificial intelligence (AI) is moving rapidly from research into everyday dental practice. Tools capable of analyzing imaging and assisting with clinical decisions are now commercially available. Systematic reviews and meta-analyses published between 2021 and 2025 demonstrate that deep learning algorithms achieve pooled diagnostic sensitivity of 0.94 for approximal caries detection on bitewing radiographs, 0.93 for periapical radiolucent lesion detection with high GRADE certainty of evidence, and 0.88 for periodontal bone loss assessment. As of late 2025, more than 44 AI-enabled dental devices had received US Food and Drug Administration 510(k) clearance as Class II Software as a Medical Device, and robotic-assisted implant surgery systems demonstrated mean angular deviations below 1.5 degrees in clinical series exceeding 270 placements. These developments raise a question the profession must now answer: at what point does the availability of validated AI tools cross from a competitive option into the standard of care? Early empirical liability research suggests that juror perceptions may increasingly favor clinicians who incorporate validated AI decision-support tools, although dental-specific legal precedent remains limited. This article reviews the clinical evidence across dental disciplines, traces the historical pattern by which technologies have reshaped practice expectations, analyzes the medicolegal framework for professional accountability, and proposes guidelines for responsible integration alongside a discussion of limitations, including automation bias, algorithmic fairness, and the gap between regulatory clearance and independent scientific validation.

PMID:
42709026
Bibliographic data and abstract were imported from PubMed on 08 Sep 2026.

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