Artificial intelligence (AI)-driven learning is transforming education, requiring educators to quickly develop the skills to integrate AI tools effectively so they complement rather than replace traditional teaching practices. The fast pace of generative AI development poses challenges, particularly for less tech-savvy teachers or those who delay learning about these tools, leaving them at risk of falling behind. This is further compounded by students' quick adaptation to widely available models such as ChatGPT-3.5 and Deepseek R1, which they increasingly use for learning, assignments, and assessments. Despite existing discussions on AI in education, there is a lack of practical guidance on how medical educators can effectively and responsibly implement AI tools in teaching. This perspective provides a practical guide for medical educators to effectively incorporate AI tools to complement their teaching strategies, generate student assessments and to adapt assignments suitable for the AI era. We address challenges such as data bias, accuracy, and ethics, ensuring AI enhances rather than undermines medical training when aligned with sound pedagogical principles. This review provides a practical, structured approach for educators, offering clear recommendations to help bridge the gap between AI advancements and effective teaching methodologies in medical education.

The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines.

The skin, the largest organ in the human body, serves both as a mechanical barrier and an autonomous lymphoid organ, protecting against various environmental threats while maintaining the balance and functionality of multiple bodily systems. This relationship extends beyond the skin itself, involving other organs closely linked to skin homeostasis and related diseases. However, systematic reviews in this area are still lacking. This review seeks to explore this bidirectional communication, with a particular focus on the critical role of the immune system. We present a comprehensive review of the latest evidence, highlighting the fundamental roles of immune cells and cytokines within the skin–organ axis, particularly IL-17A, which appears to interact with nearly all organs, illustrating their interplay and impact on skin health. Additionally, we discuss the implications of these interactions for the design and application of skin-on-a-chip and organ-on-a-chip technologies, emphasizing the importance of understanding these relationships for developing physiologically relevant in vitro models. By providing a comprehensive analysis of these complex interactions, this review establishes a solid theoretical foundation for the prevention, diagnosis, and treatment of diseases associated with the skin–organ axis, particularly regarding immune cells, cytokines, microorganisms, and their metabolites, ultimately aiming to advance research in related fields and offer new insights for clinical applications.

DeepSeek-R1 is an open-source Large Language Model (LLM) with advanced reasoning capabilities. It has gained significant attention for its impressive advantages including low costs and visualized reasoning steps. Recent advancements in reasoning LLMs like ChatGPT-o1 have significantly exhibited their considerable reasoning potential, but the closed-source nature of existing models limits customization and transparency, presenting substantial barriers to their integration into healthcare systems. This gap motivates the exploration of DeepSeek-R1 in the medical field. Thus, we comprehensively review the transformative potential, applications, and challenges of DeepSeek-R1 in healthcare. Specifically, we investigate how DeepSeek-R1 can enhance clinical decision support, patient engagement, and medical education to help for clinic, outpatient and medical research. Furthermore, we critically evaluate challenges including modality limitations (text-only), hallucination risks, and ethical issues, particularly related to patient autonomy and safety-focused recommendations. By assessing DeepSeek-R1′s integration potential, this perspective highlights promising opportunities for advancing medical AI while emphasizing necessary improvements to maximize clinical reliability and ethical compliance. This paper provides valuable guidance for future research directions and elucidates practical application scenarios for DeepSeek-R1′s successful integration into healthcare settings.

Immune checkpoint inhibitors (ICIs) have transformed cancer immunotherapy, but their clinical efficacy varies significantly due to tumor heterogeneity and patient-specific factors. Existing databases lack comprehensive integration of ICI efficacy data and fail to explore biomarkers across pan-cancer contexts, limiting their utility in precision oncology. To address this gap, we developed ImmunoCheckDB, a systematic platform that curates 173 studies on cancer ICI treatment, integrating survival outcomes for traditional and network meta-analyses with multiomic data sets from public repositories, including over 93,000+ individuals across 18 cancer types and 30 ICI regimens to provide a robust resource for pan-cancer biomarker discovery. Equipped with online tools for meta-analysis, network meta-analysis, and multiomic profiling, ImmunoCheckDB enables researchers to investigate correlations between ICI efficacy and molecular biomarkers, featuring key functionalities such as real-time visualization of forest plots, funnel plots, and network diagrams, as well as association analyses linking multiomic data to clinical outcomes. Uniquely combining meta-analytical with multiomic exploration, our platform offers insights into optimal patient populations for ICI therapy, thereby bridging the gap between clinical data and molecular research to empower researchers in advancing precision immunotherapy, with access available at https://smuonco.shinyapps.io/ImmunoCheckDB/ to democratize data-driven insights for personalized cancer treatment.