Cancer Cytopathol. 2026 Jun;134(6):e70097. doi: 10.1002/cncy.70097.
ABSTRACT
BACKGROUND: Proficiency in cytopathologic diagnosis depends heavily on extensive hands-on practice and immediate error correction. Traditional teaching models, however, are constrained by limited practice opportunities and delayed feedback, which fails to meet the core skill-development needs of residents.
METHODS: In total, 45 pathology residents were enrolled and assigned to two groups. The experimental group (n = 20) adopted a tripartite teacher-artificial intelligence-resident collaborative teaching model, whereas the control group (n = 25) received conventional instruction. Both groups underwent an identical 8-week teaching cycle.
RESULTS: The questionnaire results from the experimental group indicated that 19 of 20 residents (9%) deemed the new model highly necessary, and 15 of 20 (75%) believed it significantly improved their diagnostic competence. Semistructured interviews further revealed that the model enhanced diagnostic ability, facilitated personalized learning, and alleviated learning anxiety. For objective metrics, the experimental group demonstrated a significantly higher postintervention concordance rate for gray-zone cell identification (78.65%) compared with both their preintervention baseline (64.38%) and the contemporaneous control group (66.84%; t = 8.962; p < .001). In addition, the experimental group exhibited a markedly faster diagnostic speed (mean ± standard deviation, 3.05 ± 0.52 minutes per case) compared with their preintervention performance (5.92 ± 0.85 minutes per case) and the control group (5.63 ± 0.79 minutes per case; t = 14.821; p < .001). No statistically significant changes were observed in the control group (p > .05).
CONCLUSIONS: This study demonstrates that artificial intelligence technology integrated with real-time visual interaction effectively improves the cytopathologic diagnostic skills of residents and merits wider promotion in pathology education.
PMID:42145147 | DOI:10.1002/cncy.70097
