Evaluating DeepSeek artificial intelligence’s accuracy and clinical utility in dentistry

Objective: This study evaluates the efficacy of DeepSeek artificial intelligence (AI), a large language model, in addressing dentistry-related questions regarding diagnostic, clinical, and educational domains of dentistry. Methods: We subjected DeepSeek AI to 150 dental questions spanning oral pathology, radiology, treatment planning, and socioeconomic aspects. The questions included multiple-choice items from undergraduate examinations, case-based scenarios with radiographic images, and comparative analyses of global dental practices. Performance was assessed for accuracy, reference quality, and reasoning transparency. Results: DeepSeek AI achieved 99.3% accuracy (149/150 correct), excelling in: (1) Differential diagnosis (e.g., Behçet’s disease, acquired immunodeficiency syndrome). (2) Treatment planning (95/100 score for complex cases). (3) Global dental practice comparisons (referencing American Diabetes Association, Centers for Disease Control and Prevention, and World Health Organization guidelines). Its sole error involved a nuanced malignant tumor case lacking systemic symptom cues. The model demonstrated advanced reasoning by: (a) Deconstructing questions via keyword analysis. (b) Cross-referencing authoritative sources (e.g., Neville’s Oral Pathology , PubMed). (c) Providing layered explanations for conceptual queries. Conclusion: DeepSeek AI exhibits near-perfect accuracy in dental knowledge synthesis. Its open-source framework and adaptive learning positions it as a transformative tool for clinical decision support, dental education, and career guidance.