自闭症谱系障碍（ASD）是一类神经发育障碍性疾病，其核心临床表现为社交行为障碍、语言沟通困难以及重复刻板行为。早期诊断并进行早期行为干预，是目前能改善患者临床症状且减少终身残疾的主要措施。但早期诊断困难，因为目前所采用的行为量表，比如ABC（异常行为检查表）等，在儿童生命早期阶段，如2岁左右，很难准确地反应出被试者的社交行为或者语言交流能力；而且，其判断也非常依赖于医生的经验，具有一定的主观性。近年来，随着脑影像技术，比如核磁共振（MRI），特别是功能性核磁共振（fMRI），以及人工智能技术（AI）的飞速发展，为ASD的早期诊断，从无创、客观、以及神经活动异常-行为异常偶联的角度，展示了一种新的机遇。本文比较系统地介绍了ASD的临床基础与诊断挑战，并详细阐述了AI在ASD早期诊断中的数据来源、核心算法和典型研究案例。通过分析当前技术面临的挑战与局限性，提出未来研究方向与临床转化路径，以期进一步加强AI在ASD早期诊断中的临床使用研究，使之最终能高效的服务于临床。

   Autism spectrum disorder（ASD）is a group of neurodevelopmental disorders clinically characterized by deficits in social interaction and verbal communication，along with repetitive behaviors．Early diagnoses，followed by early behavioral interventions，are currently effective strategies to attenuate clinical symptoms and improve patient outcomes in ASD; however，early diagnosis remains challenging at present．Current diagnostic tools，such as the Aberrant Behavior Checklist（ABC），have difficulty in accurately evaluating social engagement and verbal communication in early childhood（e.g. ，at 2 years of age）．Furthermore，such evaluations may be somewhat subjective，as they rely，at least partially，on clinicians’ experience．Recently，with the rapid development of brain imaging techniques（e.g. ，MRI，especially fMRI）and artificial intelligence（AI），a significant opportunity has emerged to develop novel early-diagnostic tools for ASD．These tools exhibit clear advantages，including being non-invasive，more objective，and focusing on the coupling of abnormalities in both neuronal activity and behavioral responses．Here，we systematically introduce the clinical features of ASD，discuss the challenges in early diagnosis，and review the current status of AI-driven diagnostic tool development in detail，including data acquisition methods，computational architectures，and typical clinical applications．Through a detailed analysis of the technical challenges，advantages，and limitations of these tools，we propose future research directions，aiming to eventually establish highly valuable AI-based diagnostic approaches for clinical use in ASD care．

      目的  本文聚焦DeepSeek这一国产人工智能技术，结合护理临床实践，系统探讨其在护理场景中的应用潜力、现存问题及应对策略。方法  检索国内外相关文献，与现有通用人工智能技术对比，进行综述，并提出思考和建议。结果  预计DeepSeek在护理文书自动化、个性化护理方案生成、临床决策支持、护理质控及教育培训等提供适配应用路径，针对性的服务和解决方案等。结论  DeepSeek可通过多模态技术整合与跨平台互补策略，推动护理服务向智能化、精准化方向发展，为缓解护理人力短缺、优化资源分配提供新思路。

     Objective  This study focuses on DeepSeek，a domestic artificial intelligence technology，systematically exploring its application potential，existing issues，and targeted strategies in nursing clinical scenarios through integration with practical nursing care contexts．Methods  Relevant literatures from both domestic and international sources were collected，compared with existing Artificial General Intelligence（AGI）technologies，to conduct a review，and  propose  reflections and recommendations．Results  Through literature review and technical comparisons，the  results proposed specific application paths for DeepSeek in scenarios such as automated nursing documentation，personalized care plan generation，clinical decision support，quality control，and education．It further addressed issues including data privacy，ethical risks，and technical limitations．Conclusions  The findings suggest that DeepSeek can integrate multimodal technologies and cross-platform complementary strategies to promote intelligent and precise nursing services，offering innovative solutions to alleviate nursing shortages and optimize resource allocation．