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진단검사치의학 분야에서 인공지능 기술의 활용과 미래

Artificial intelligence in diagnostic and laboratory dentistry: Current applications and future perspectives

Abstract

This review comprehensively examines the current status, clinical applicability, and future directions of artificial intelligence (AI) in diagnostic and laboratory dentistry. Dental AI primarily utilizes deep learning models based on radiographic and histopathological images to assist in diagnosis of conditions including dental caries, periodon tal disease, periapical lesions, implant-related assessments, temporomandibular disorders, and oral squamous cell carcinoma (OSCC), enhancing diagnostic sensitivity and specificity. Recently, AI applications have expanded to salivary and blood biomarker analyses, integrating inflammatory cytokines (IL-1β, TNF-α, MMP-8), OSCC-related proteins (CYFRA 21-1, SCC-Ag, p53), salivary microbiome profiles, and blood-based indicators such as C-reactive protein, HbA1c, and bone metabolism markers, enabling predictive and personalized diagnostic modeling. The potential use of large language models (LLMs) has garnered attention, offering capabilities for analyzing elec tronic health records and clinical text data to support diagnosis, recommend treatment strategies, and assist in patient counseling and education. In the United States, several dental AI platforms, including Pearl Inc.’s Second Opinion®, have received FDA 510(k) clearance and are entering clinical practice, while in Korea, commercializa tion is progressing through Ministry of Food and Drug Safety approvals. Nevertheless, challenges remain, includ ing insufficient data standardization, limited multi-institutional datasets, legal and ethical considerations, and in tegration with clinical workflows. To address these issues, multi-institutional prospective validation, development of generalizable models, multimodal AI research, and implementation of explainable AI are necessary. Overall, dental AI is evolving beyond image interpretation toward a multimodal clinical decision support system that in tegrates imaging, biomarkers, clinical information, and LLMs to support personalized diagnostics and treatment planning after validation.. (J Korean Dent Assoc 2026; 64(5): 174-183)

keywords
Dentistry, Diagnosis, Clinical Laboratory Techniques, Artificial Intelligence, Deep Learning, Large Language Models

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