Recent studies have highlighted the potential of ferroptosis in treating breast cancer. However, the efficacy of ferroptosis induction in the most common subtype, estrogen receptor-positive (ER + ) breast cancer, remains inadequately explored. This study unveils that both short-term and long-term treatment with ER-targeted endocrine agents sensitizes ER+ breast cancer cells to ferroptosis inducers, particularly the GPX4 inhibitor, revealing a non-mutational sensitization mechanism. Based on this finding, we introduce a 55-gene signature score (FERscore) tailored to assess ferroptosis susceptibility in breast cancer. Data from cell lines and primary tumors demonstrate significant lower FERscores in ER+ breast cancer compared to other subtypes; however, FERscores dramatically increase in endocrine-resistant ER+ tumor cells and residual tumors post-endocrine therapy. Furthermore, FERscore correlates positively with mesenchymal traits, stemness, immune cell infiltration, and cancer-associated fibroblasts enrichment, while inversely correlating with estrogen responsiveness and DNA repair capacity. Additionally, the FERscore proves effective in predicting therapeutic responses to anti-ER, anti-HER2, poly (ADP-ribose) polymerase inhibitor, and anti-angiogenesis therapies in breast cancer. In summary, ferroptosis induction emerges as a promising avenue in breast cancer therapy. The FERscore offers an innovative tool for identifying patients who may benefit from ferroptosis-inducing therapies, especially those responsive to GPX4 inhibitors.

Background: Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education. Objective: This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention. Methods: This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient>0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances. Results: Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P<.001; Cohen d=0.49). OSCE total scores were significantly higher (mean 80.95, SD 7.57 vs mean 76.24, SD 9.23; mean difference=4.71, 95% CI 2.31-7.11; P<.001; d=0.56), with clinical reasoning showing the largest effect (mean difference=2.22, 95% CI 1.18-3.25; P<.001; d=0.61). Analysis of covariance adjusted results remained significant for all primary outcomes (adjusted knowledge difference=3.52, P=.002; adjusted OSCE difference=4.52, P<.001). At 3-month follow-up (174/190, 91.6%), the AI-assisted group maintained higher knowledge scores (mean 77.36, SD 8.60 vs mean 72.84, SD 10.42; mean difference=4.52, 95% CI 1.68-7.36; P=.002; d=0.47), with similar knowledge decay rates between groups. Conclusions: This study provides the first systematic evidence that integrating AI tools within structured peer teaching enhances orthopedic clinical education across multiple domains, including knowledge acquisition, OSCE performance, and student engagement. Unlike prior studies examining AI as a stand-alone learning tool, this work demonstrates the synergistic potential of combining AI knowledge support with peer teaching’s social learning benefits, with particularly strong effects on clinical reasoning. These findings support scalable, cost-effective implementation of AI-augmented peer teaching, though randomized controlled trials are needed to confirm causality and determine optimal implementation strategies.

BACKGROUND Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education. OBJECTIVE This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention. METHODS This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient>0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances. RESULTS Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P<.001; Cohen d=0.49). OSCE total scores were significantly higher (mean 80.95, SD 7.57 vs mean 76.24, SD 9.23; mean difference=4.71, 95% CI 2.31-7.11; P<.001; d=0.56), with clinical reasoning showing the largest effect (mean difference=2.22, 95% CI 1.18-3.25; P<.001; d=0.61). Analysis of covariance adjusted results remained significant for all primary outcomes (adjusted knowledge difference=3.52, P=.002; adjusted OSCE difference=4.52, P<.001). At 3-month follow-up (174/190, 91.6%), the AI-assisted group maintained higher knowledge scores (mean 77.36, SD 8.60 vs mean 72.84, SD 10.42; mean difference=4.52, 95% CI 1.68-7.36; P=.002; d=0.47), with similar knowledge decay rates between groups. CONCLUSIONS This study provides the first systematic evidence that integrating AI tools within structured peer teaching enhances orthopedic clinical education across multiple domains, including knowledge acquisition, OSCE performance, and student engagement. Unlike prior studies examining AI as a stand-alone learning tool, this work demonstrates the synergistic potential of combining AI knowledge support with peer teaching’s social learning benefits, with particularly strong effects on clinical reasoning. These findings support scalable, cost-effective implementation of AI-augmented peer teaching, though randomized controlled trials are needed to confirm causality and determine optimal implementation strategies.