Objectives: This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural networks (CNNs): Alexnet, VGG-16, and GoogLeNet; the performances of the constructed systems were evaluated. Methods: One oral radiologist classified 1500 panoramic radiographs into three types. In C1, the endosteal margin of the cortex was smooth and sharp, whereas porosities were observed in C2 and C3. The risks of osteoporosis were higher in C2 and C3 than in C1; C3 had the highest risk. This information was included with the images as training data; three CNNs were transfer trained. Using each trained CNN, the diagnostic accuracy was assessed using panoramic radiographs and bone mineral density inspection findings in the lumbar spine and femoral neck of 100 additional patients. Results: All CNNs exhibited relatively good agreement with the oral radiologist’s judgement (86.0%–90.7%). The predictive results of the three systems for osteoporosis of the lumbar spine showed sensitivities of 78.3%–82.6%, specificities of 71.4%–79.2%, and accuracies of 74.0%–79.0%. The predictive results for osteoporosis of the femoral neck showed sensitivities of 80.0%–86.7%, specificities of 67.1%–74.1%, and accuracies of 70.0%–75.0%. Conclusions: The constructed systems were generally more accurate than the previously developed conventional system. The new systems may facilitate osteoporosis prediction and prevent subsequent fractures by encouraging patients with suspected osteoporosis to undergo further inspections (e.g., dual-energy X-ray absorptiometry) and treatment.

/purpose: Large language models (LLMs) have been studied in text-based healthcare tasks, but their performance in multimodal dental applications has not yet been fully explored. This study evaluated the performance of four multimodal LLMs on dental licensing examination questions with both text-only and visually-based components. Four multimodal LLMs, ChatGPT-4o (4o), OpenAI o1 (o1), Claude 3.5 Sonnet (Sonnet), and Gemini 2.0 Flash Thinking Experimental (Gemini), were tested on 353 questions from the 2024 Japanese National Dental Examination, including 204 text-only and 149 visually-based questions spanning 17 dental specialties. A zero-shot approach was used without prompt engineering. Performance was analyzed using Cochran's Q test and McNemar's test with Bonferroni correction. o1 achieved the highest overall correct response rate (81.9 %), followed by Sonnet (71.7 %), Gemini (66.6 %), and 4o (65.7 %). All models performed significantly better on text-only questions (79.9–92.2 %) than on visually-based questions (45.6–67.8 %). Performance varied by specialty, with highest scores in basic medical sciences (Dental pharmacology: 100 %; Oral physiology: 86.7–100 %) and lower scores in clinical specialties requiring visual interpretation (Orthodontics: 36.4–66.7 %). Multimodal LLMs demonstrate promising performance on dental examination questions, particularly in text-based scenarios, but significant challenges remain in complex visual interpretation. The remarkable zero-shot performance of newer models such as o1 suggests potential applications in dental education and certain aspects of clinical decision support, although further advances are needed before reliable application in visually complex diagnostic workflows.