ArticleBMC medical education2026
Clinical decision-making skill in diagnosing of oral diseases among medical interns.
Article in BMC medical education, 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
backgroundOral mucosal diseases are frequently misdiagnosed due to overlapping clinical presentations. Given the important role of physicians in the initial assessment of oral conditions, evaluating diagnostic reasoning among medical trainees is essential. This study aimed to assess the clinical decision-making skills of medical interns in diagnosing oral diseases.
methodsThis cross-sectional analytical study was conducted among 66 medical interns at Guilan University of Medical Sciences using a census sampling approach. Data were collected using a two-part questionnaire consisting of demographic information and 19 Key Feature-based clinical scenarios, most accompanied by visual aids. The instrument was validated using a two-round Delphi process and demonstrated acceptable reliability (Cronbach's alpha = 0.70; ICC = 0.861). Data were analyzed using descriptive statistics, independent t-tests, and linear regression analysis.
resultsThe mean diagnostic performance score was 7.14 ± 2.9 (range: 1.73-13.77). Male interns had higher scores than females (p = 0.003). No significant association was found between age and diagnostic performance, while GPA showed a significant positive correlation (p < 0.001). Diagnostic accuracy was higher for conditions with more recognizable clinical features, such as oral candidiasis and denture stomatitis, whereas lower accuracy was observed for conditions with non-specific presentations, including reflux-related lesions, burning mouth syndrome, and xerostomia.
conclusionMedical interns demonstrated suboptimal diagnostic performance in oral diseases. These findings suggest a need for enhanced training in oral diagnosis and clinical reasoning within medical curricula. Future educational strategies incorporating case-based learning and interdisciplinary training may improve diagnostic competence.
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