Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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International dental journal
Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis
INTRODUCTION: Large language models (LLMs), including OpenAI's GPT family accessed via interfaces such as ChatGPT and Microsoft Copilot, as well as non-GPT systems such as Google Gemini, are increasingly applied in healthcare and dental education. However, the accuracy of these systems in specialized tasks such as answering dental examination questions remains unclear. METHODS: This systematic review and meta-analysis evaluated LLM performance in answering dental questions. Databases searched were PubMed, Embase, Scopus, and Web of Science. Data on question type and number, LLM versions, and accuracy rates were extracted. Pooled accuracy was estimated using a random-effects model; heterogeneity and publication bias were assessed. RESULTS: A total of 39 studies were included, with ChatGPT-4 being the most frequently evaluated model. The pooled accuracy for LLMs was 63.7% (95% CI: 60.3%-67.1%), with high heterogeneity (I² = 91.5%). Subgroup analysis revealed ChatGPT-4 and Copilot (a GPT-based interface) achieved the highest pooled accuracies (∼73% and ∼75%, respectively). Direct comparisons confirmed ChatGPT-4 significantly outperformed earlier versions and some competitor models. Sensitivity analyses supported the robustness of findings. CONCLUSION: LLMs demonstrate moderate accuracy in answering dental examination questions and are currently insufficient for autonomous clinical decision-making. When their limitations are explicitly recognized, however, these systems may serve as valuable adjuncts in dental education and examination preparation. Methodological strategies such as structured prompting and retrieval-augmented approaches warrant further investigation but were not the primary focus of the present analysis.
2 Aug 2026
Read appraisal →JMIR medical informatics
Large Language Models for Endodontic Symptom Assessment and Treatment Planning Using Image-Free Clinical Records: Comparative Evaluation Study
BACKGROUND: Accurate assessment of pulpal status is essential for achieving successful endodontic outcomes. However, direct evaluation remains inherently challenging because the pulp is surrounded by calcified tissue, necessitating reliance on clinical and radiographic examinations for diagnostic and prognostic decision-making. These procedures demand substantial clinical expertise and time, and less-experienced clinicians often face challenges that may lead to errors in diagnosis and treatment planning. Recent advancements in large language models (LLMs) offer promising opportunities to enhance clinical reasoning by facilitating the integration of evidence and supporting methodical diagnostic decision-making. OBJECTIVE: This study aimed to evaluate the clinical applicability of LLMs by comparing their text-based clinical screening performance and the clinical validity of their treatment plan responses with those of human evaluators. METHODS: Between January 2011 and December 2022, 100 clinical cases involving primary endodontic disease were randomly selected from the clinical records of outpatients who visited the Department of Conservative Dentistry or Advanced General Dentistry (AGD) at Yonsei University Dental Hospital. Four prompt types, combining 2 variables (language and role), were used as input for 4 LLMs. Both LLMs and human evaluators (AGD specialists, AGD residents, endodontic residents, and senior dental students) assessed the cases using text-based clinical records. Radiographic images were not directly provided. Screening performance was evaluated using a 0-to-2-point concordance scale, and treatment plan validity and relevance were assessed using a 5-point Likert scale. RESULTS: Among the 4 LLMs evaluated, ChatGPT achieved the highest mean concordance score on Korean-doctor prompts (mean 0.98, SD 0.82). However, this score did not reach the partially correct criterion of 1 on the 0 to 2-point scale. Clova X recorded the lowest mean score on English-patient prompts (mean 0.23, SD 0.63). Across both diagnostic categories, AGD specialists demonstrated the highest diagnostic accuracy (pulpal: 0.70; periapical: 0.65), with higher sensitivity but lower specificity than those exhibited by the other groups. ChatGPT also showed favorable performance among the LLMs, with accuracies of 0.65 (95% CI 0.55-0.74) for pulpal disease and 0.57 (95% CI 0.47-0.69) for periapical disease, which were comparable to those of AGD and endodontic residents. CONCLUSIONS: Under image-free clinical record review conditions, ChatGPT 4.0 showed relatively higher and more consistent performance in symptom screening and treatment planning compared to the other LLMs evaluated. However, its highest mean score of 0.98 (SD 0.82) did not reach the partially correct criterion of 1 on the 0 to 2-point scale. Hallucinations generated by LLMs and experience-dependent interpretation biases among human evaluators remain key challenges that require attention. Therefore, continuous clinical supervision and comprehensive user training are necessary for the safe and effective clinical application.
25 July 2026
Read appraisal →BMC oral health
The association between oral hygiene and metabolic dysfunction-associated steatotic liver disease - a systematic review
BACKGROUND: Given the increasingly evident links between oral health and systemic health, the aim of this review was to investigate whether oral hygiene practices influence the risk or progression of metabolic dysfunction-associated steatotic liver disease (MASLD). The findings support the potential importance of good oral hygiene and regular dental visits, especially for patients with MASLD. METHODS: This systematic review was registered with PROSPERO (CRD420251041530), and PubMed, Scopus, Embase and Web of Science were used for literature research up to June 2026. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, screening, study selection and quality check were performed using Rayyan and the Joanna Briggs Institute (JBI) critical appraisal checklists. Studies assessing the potential link between the frequency of oral hygiene procedures and the incidence, prevalence or progression of MASLD were selected for a systematic synthesis. The old terms non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) were considered as well. RESULTS: A total of 570 results were identified of which six studies met the inclusion criteria. Five of them reported a correlation between higher tooth brushing frequency and a lower incidence, prevalence or progression of NAFLD/NASH compared to individuals with lower tooth brushing frequency. Notably, one study revealed significantly worse liver parameters among NASH patients without regular dental visits than among those who visited a dentist more than once a year. CONCLUSIONS: An association between oral hygiene habits and NAFLD/NASH was found in all included studies. A greater frequency of daily tooth brushing as well as regular dental visits might have beneficial effects on both the development and progression of fatty liver disease. However, there is not enough evidence to establish causality yet and the impact of confounding factors must be further investigated. CLINICAL TRIAL REGISTRATION: Not applicable.
11 July 2026
Read appraisal →Science advances
A battery-free wireless intelligent aligner for spatially resolved, closed-loop theranostics of chronic oral diseases
Intelligent wearable theranostic systems are advancing personalized health care by enabling continuous monitoring and proactive therapy. However, managing chronic oral diseases such as caries and periodontitis is challenging due to the oral cavity's complex, spatially heterogeneous microenvironments and limited space for powered intraoral electronics. To address this, we developed a wireless, battery-free, and wearable intelligent therapeutic aligner (WiB-ITA) that integrates a butterfly-inspired dual-sided flexible integrated theranostic electrode array (iTEA) to simultaneously monitor compartment-matched pH and nitrite and electrochemically gate on-demand antimicrobial release. The system is powered and commanded via a relay-assisted near-field communication (NFC)-Bluetooth low energy (BLE) dual-hop link, enabling stable battery-free operation across tooth positions and full-arch usability. Validated from in vitro to preliminary human studies, WiB-ITA establishes a scalable, compartment-matched intraoral closed-loop theranostic framework. This integrated theranostic platform represents a breakthrough in personalized oral health care and provides a paradigm for managing other chronic conditions.
27 June 2026
Read appraisal →Oral health & preventive dentistry
Natural Agents for the Improvement of Gingival Health: Systematic Review and Meta-Analysis of Randomized Clinical Trials
PURPOSE: This systematic review and meta-analysis evaluated the efficacy of natural ingredients used in toothpastes and gels in improving gingival health. MATERIALS AND METHODS: A comprehensive search strategy using different databases was conducted in accordance with PRISMA guidelines to identify randomized clinical trials (RCTs) on the use of natural ingredients for improving gingival health. Included studies focussed on outcomes measuring gingival health parameters, such as gingival index (GI), or bleeding on probing (BoP). Out of 249 identified studies, 10 RCTs met the inclusion criteria. Formulations included natural ingredients from plant extracts such as Aloe vera or Pudilan, and hydroxyapatite (as tooth-like calcium phosphate). RESULTS: The meta-analysis demonstrated a statistically significant improvement in gingival health when natural ingredients were used compared to control toothpastes and gels (p=0.0051). These findings suggest that natural ingredients may offer comparable or superior efficacy to conventional oral care products in terms of gingival health. Notably, Aloe vera and hydroxyapatite consistently demonstrated clinical benefits. CONCLUSIONS: Natural ingredients used in oral care products represent a promising strategy in gingivitis prevention and management. Their use in toothpastes and gels may therefore provide clinicians and patients with evidence-based, well-tolerated alternatives or adjuncts to conventional formulations for maintaining and improving gingival health.
20 June 2026
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