Performance of AI chatbots on controversial topics in oral medicine, pathology, and radiology.

OBJECTIVES: In this study, we assessed 6 different artificial intelligence (AI) chatbots (Bing, GPT-3.5, GPT-4, Google Bard, Claude, Sage) responses to controversial and difficult questions in oral pathology, oral medicine, and oral radiology. STUDY DESIGN: The chatbots' answers were evaluated by board-certified specialists using a modified version of the global quality score on a 5-point Likert scale. The quality and validity of chatbot citations were evaluated. RESULTS: Claude had the highest mean score of 4.341 ± 0.582 for oral pathology and medicine. Bing had the lowest scores of 3.447 ± 0.566. In oral radiology, GPT-4 had the highest mean score of 3.621 ± 1.009 and Bing the lowest score of 2.379 ± 0.978. GPT-4 achieved the highest mean score of 4.066 ± 0.825 for performance across all disciplines. 82 out of 349 (23.50%) of generated citations from chatbots were fake. CONCLUSIONS: The most superior chatbot in providing high-quality information for controversial topics in various dental disciplines was GPT-4. Although the majority of chatbots performed well, it is suggested that developers of AI medical chatbots incorporate scientific citation authenticators to validate the outputted citations given the relatively high number of fabricated citations.

Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy.

INTRODUCTION: The aim of this systematic review and meta-analysis was to investigate the overall accuracy of deep learning models in detecting periapical (PA) radiolucent lesions in dental radiographs, when compared to expert clinicians. METHODS: Electronic databases of Medline (via PubMed), Embase (via Ovid), Scopus, Google Scholar, and arXiv were searched. Quality of eligible studies was assessed by using Quality Assessment and Diagnostic Accuracy Tool-2. Quantitative analyses were conducted using hierarchical logistic regression for meta-analyses on diagnostic accuracy. Subgroup analyses on different image modalities (PA radiographs, panoramic radiographs, and cone beam computed tomographic images) and on different deep learning tasks (classification, segmentation, object detection) were conducted. Certainty of evidence was assessed by using Grading of Recommendations Assessment, Development, and Evaluation system. RESULTS: A total of 932 studies were screened. Eighteen studies were included in the systematic review, out of which 6 studies were selected for quantitative analyses. Six studies had low risk of bias. Twelve studies had risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of included studies (all image modalities; all tasks) were 0.925 (95% confidence interval [CI], 0.862-0.960), 0.852 (95% CI, 0.810-0.885), 6.261 (95% CI, 4.717-8.311), 0.087 (95% CI, 0.045-0.168), and 71.692 (95% CI, 29.957-171.565), respectively. No publication bias was detected (Egger's test, P = .82). Grading of Recommendations Assessment, Development and Evaluationshowed a "high" certainty of evidence for the studies included in the meta-analyses. CONCLUSION: Compared to expert clinicians, deep learning showed highly accurate results in detecting PA radiolucent lesions in dental radiographs. Most studies had risk of bias. There was a lack of prospective studies.

Comparison of Periodontal Ligament Stem Cells with Mesenchymal Stem Cells from Other Sources: A Scoping Systematic Review of In vitro and In vivo Studies.

OBJECTIVE: The application of stem cells in regenerative medicine depends on their biological properties. This scoping review aimed to compare the features of periodontal ligament stem cells (PDLSSCs) with stem cells derived from other sources. DESIGN: An electronic search in PubMed/Medline, Embase, Scopus, Google Scholar and Science Direct was conducted to identify in vitro and in vivo studies limited to English language. RESULTS: Overall, 65 articles were included. Most comparisons were made between bone marrow stem cells (BMSCs) and PDLSCs. BMSCs were found to have lower proliferation and higher osteogenesis potential in vitro and in vivo than PDLSCs; on the contrary, dental follicle stem cells and umbilical cord mesenchymal stem cells (UCMSCs) had a higher proliferative ability and lower osteogenesis than PDLSCs. Moreover, UCMSCs exhibited a higher apoptotic rate, hTERT expression, and relative telomerase length. The immunomodulatory function of adipose-derived stem cells and BMSCs was comparable to PDLSCs. Gingival mesenchymal stem cells showed less sensitivity to long-term culture. Both pure and mixed gingival cells had lower osteogenic ability compared to PDLSCs. Comparison of dental pulp stem cells (DPSCs) with PDLSCs regarding proliferation rate, osteo/adipogenesis, and immunomodulatory properties was contradictory; however, in vivo bone formation of DPSCs seemed to be lower than PDLSCs. CONCLUSION: In light of the performed comparative studies, PDLSCs showed comparable results to stem cells derived from other sources; however, further in vivo studies are needed to determine the actual pros and cons of stem cells in comparison to each other.