Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectures and are often validated in small patient cohorts. We developed a Mamba-based deep learning model for predicting chemotherapy efficacy using needle biopsy (MCEN) from 1646 patients with breast cancer across five tertiary hospitals, aiming to predict pathological complete response following NAC. We randomly divided 1023 biopsy samples from one hospital into training and validation sets at an 8:2 ratio and used the remaining four hospitals as external test sets to evaluate the model's performance and robustness. In the training and validation sets, the MCEN achieved areas under the receiver operating characteristic curve (AUROCs) of 0.923 and 0.78, respectively. For the four external test sets, the MCEN achieved AUROCs ranging from 0.761- to 0.809. Incorporating clinicopathological information improved the MCEN model's predictive performance, achieving AUROCs of 0.937 and 0.811 in the training and validation sets, respectively, and ranging from 0.773- to 0.84 in the external test sets. Our study demonstrates the potential of the MCEN as a valuable tool in clinical decision-making.

Generative artificial intelligence (GenAI) is reshaping medical education while fostering technological dependence among students. This study employed an explanatory sequential mixed-methods design. In the quantitative phase, an empirical analysis was conducted using survey data collected from a sample of 1295 Chinese medical students. The subsequent qualitative phase involved thematic analysis of interview transcripts from 16 medical educators to elucidate the underlying mechanisms. Findings reveal that GenAI was deeply integrated into medical students' daily learning routines. Tool selection favored general-purpose platforms, whereas specialist medical tools exhibited exceptionally low utilization rates. The clinical application possibilities remained below 20% across all situations. With an overall dependency score of 21.91 ± 6.75, over 60% of students reported dependence on GenAI. Multivariate linear regression analysis indicated performance expectancy, academic pressure, and social influence showed significant positive correlations with GenAI dependency. Conversely, critical thinking exhibited a significant negative correlation. Future medical education should strategically reposition GenAI as a "cognitive scaffold" by reinforcing critical thinking and establishing standardized usage guidelines to facilitate high-quality development.

Large language models (LLMs) are being deployed in clinical settings despite an underdeveloped evidence base regarding their real-world effectiveness. This study employed systematic evidence mapping to characterize outcome measures used in published studies and registered clinical trials (Jan 2022-Jun 2025) evaluating LLM performance. Analysis of 55 included studies revealed a predominance of human-AI collaborative designs (65.5%) for decision support and symptom management. LLM-only interventions focused on functional performance and operational or process impact outcomes (e.g., accuracy and time saving), whereas LLM-assisted interventions showed positive clinical effects, particularly in psychological health endpoints. Critical evidence gaps persist: diagnostic accuracy in randomized trials was notably lower and more variable (range 0.65-0.88) compared to non-randomized studies (typically ≥ 0.80); clinical efficiency impacts were inconsistent, and reporting quality was suboptimal (78.8% mean CONSORT-AI adherence), with critical omissions in handling data quality and performance errors. These findings indicate a heterogeneous and insufficient evidence landscape, necessitating standardized core outcome sets, mandatory use of specialized reporting guidelines, and robust clinical trials to ensure the safe integration of LLMs.