ArticleHealthcare (Basel, Switzerland)2026
AI-Augmented Digital Diagnostics in Minimally Invasive Dentistry: A Case Report on Clinically Occult Approximal Caries.
Article in Healthcare (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundApproximal dental caries pose a persistent diagnostic challenge, as early lesions frequently remain clinically occult beneath intact enamel surfaces during routine visual-tactile inspection. This case report describes the clinical utility, limitations, and decision-making discrepancies within a multimodal digital diagnostic workflow combining intraoral radiography, near-infrared light transillumination (NILT), and deep-learning artificial intelligence (AI) radiographic analysis. CASE PRESENTATION: A 19-year-old female patient presented with provoked cold and sweet sensitivity localized to the upper left quadrant. Clinical inspection revealed visually intact accessible enamel surfaces (ICDAS 0 on tooth 25. To resolve the discrepancy between subjective symptoms and inconclusive initial visual findings, periapical radiography and NILT were performed. After the clinician-led treatment plan had been established, the radiograph was additionally processed using ClinicalAID as a parallel research analysis. Diagnostic Assessment and Management: Periapical radiography and NILT revealed approximal demineralization on teeth 24, 25, and 26. After the clinician-led treatment plan had been established, the radiograph was additionally processed with ClinicalAID as a parallel research analysis, yielding model confidence scores of 100%, 99%, and 98% for teeth 24, 25, and 26, respectively. Pre-operative therapeutic stratification remained entirely clinician-led: teeth 24 and 25 were selected for operative intervention based on provoked symptomatology and radiographic appearance of dentinal involvement, whereas tooth 26 was managed non-operatively with topical fluoridation and scheduled monitoring. Operative clinical reference assessment confirmed demineralization extending into the middle third of the dentin on tooth 25.
conclusionsHigh AI caries-detection confidence scores do not differentiate between lesion depth, cavitation status, or the necessity of operative intervention, as evidenced by divergent clinical pathways despite near-identical AI output scores on teeth 25 and 26. Deep-learning algorithms must function strictly as adjunctive decision-support tools within a human-in-the-loop framework (Intelligence Augmentation), with treatment planning governed by clinical symptoms, caries risk, and conventional diagnostic synthesis.
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