[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3281":3,"related-3281":38},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"search_phrases":21,"slug":22,"view_count":15,"doi":23,"paper":24,"created_at":37},3281,"Economic Growth Nowcasting in Mauritania using Machine Learning and Satellite Data","https:\u002F\u002Fdoi.org\u002F10.63620\u002Fmkijbaft.2026.1027","This paper examines the role of satellite data and machine learning techniques in improving the nowcasting of real GDP in Mauritania, a context characterized by limited data availability and delays in official statistics. By combining traditional macroeconomic indicators with satellite-based variables such as Nighttime Lights and the Normalized Difference Vegetation Index (NDVI), the study develops a framework capable of capturing real-time economic dynamics. The results show that the inclusion of satellite data improves predictive accuracy, particularly when using non-linear models such as XGBoost. In particular, XGBoost records a reduction in RMSE from 0.019 to 0.017 and an increase in R² from 0.372 to 0.483 when satellite variables are included. Nighttime Lights are strongly correlated with economic activity, while NDVI exhibits more limited explanatory power. Overall, the findings highlight the potential of integrating alternative data sources and machine learning methods to enhance economic monitoring and support decision-making in data-constrained environments.","本文考察了卫星数据和机器学习技术在改进毛里塔尼亚实际GDP即时预测（nowcasting）中的作用，该国面临数据可得性有限和官方统计数据滞后的背景。通过将传统宏观经济指标与夜间灯光（Nighttime Lights）和归一化植被指数（NDVI）等卫星变量相结合，本研究构建了一个能够捕捉实时经济动态的框架。结果表明，纳入卫星数据提高了预测精度，尤其是在使用XGBoost等非线性模型时。具体而言，当纳入卫星变量后，XGBoost的均方根误差（RMSE）从0.019降至0.017，R²从0.372提升至0.483。夜间灯光与经济活动高度相关，而NDVI的解释力则较为有限。总体而言，研究结果凸显了整合替代数据源和机器学习方法以增强经济监测并支持数据受限环境下决策的潜力。",null,"OpenAlex","2026-09-22T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究毛里塔尼亚GDP即时预测，与三农、农业信息化、智慧农业无关，不予入选。",[19],{"name":10,"url":6},[12],[],"EconomicGrowthNowcastinginMauritaniausin-3281","10.63620\u002Fmkijbaft.2026.1027",{"doi":23,"openalex_id":25,"authors":26,"venue":9,"cited_by_count":15,"oa_url":6,"card":29,"direction":35,"ingested_from":36},"W7213984344",[27],{"name":28,"orcid":9},"Yahya Abou LY",{"tldr":30,"method":31,"finding":32,"direction":33,"opportunity":34},"结合卫星数据与机器学习改进毛里塔尼亚实时GDP预测。","使用夜间灯光和NDVI卫星变量，结合XGBoost等非线性模型。","加入卫星数据后XGBoost的RMSE降低、R²提升，夜间灯光解释力强。","农业遥感与作物表型","可探索多源卫星数据融合与可解释AI，提升数据稀缺地区经济监测精度。","农业人工智能与决策模型","openalex","2026-09-23T23:30:35.177095Z",{"total":39,"page":40,"page_size":39,"items":41},6,1,[42,74,120,154,182,213],{"id":43,"title":44,"url":45,"summary":46,"summary_zh":47,"content":9,"source_name":48,"source_url":45,"published_at":49,"category":12,"cover_url":9,"hotness":50,"is_selected":14,"score":15,"score_detail":51,"sources":53,"tags":57,"search_phrases":58,"slug":59,"view_count":15,"doi":60,"paper":61,"created_at":73},3346,"The Confluence of AI and Eco-Innovation: Shaping the Future of Multidisciplinary Research","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22912613","Abstract Artificial Intelligence (AI) and eco-innovation are increasingly converging to redefine how societies address environmental degradation, resource scarcity, and climate risk. This paper examines the intersection of these two domains as an emerging site of multidisciplinary inquiry, one that draws simultaneously on computer science, environmental economics, engineering, and policy studies. Rather than treating AI merely as a technical enabler bolted onto existing green technologies, the paper argues that AI is reshaping the very logic of eco-innovation—accelerating discovery cycles, enabling predictive resource management, and creating new business models built around circularity and efficiency. Drawing on a structured review of academic literature, industry reports, and policy documents published largely over the past decade, the study identifies recurring themes: AI-driven materials discovery, smart energy grids, precision agriculture, and algorithmic carbon accounting. It also surfaces tensions that are often underexplored, including the substantial energy footprint of AI systems themselves, questions of data governance in environmental monitoring, and the risk of techno-solutionism crowding out structural policy reform. Using a qualitative-interpretive methodology combined with thematic content analysis, the paper maps the current research landscape and proposes a framework for understanding AI-eco-innovation convergence across four layers: technological, organizational, institutional, and ecological. The paper concludes that meaningful progress in this space depends not on AI alone but on deliberate multidisciplinary collaboration among technologists, environmental scientists, economists, and regulators. It offers directions for future research, including longitudinal impact studies and frameworks for measuring the net environmental value of AI-enabled innovation.","摘要 人工智能（AI）与生态创新正日益融合，重新定义社会应对环境退化、资源稀缺和气候风险的方式。本文考察这两个领域的交汇点，将其视为一个新兴的多学科研究场域，同时涉及计算机科学、环境经济学、工程学和政策研究。本文并不将AI仅仅视为附加于现有绿色技术之上的技术赋能工具，而是认为AI正在重塑生态创新的内在逻辑——加速发现周期、实现预测性资源管理，并围绕循环性与效率创造新的商业模式。基于对过去十年间主要发表的学术文献、行业报告和政策文件的结构化综述，本研究识别出反复出现的主题：AI驱动的材料发现、智能电网、精准农业和算法碳核算。研究还揭示了常被忽视的张力，包括AI系统自身的巨大能源足迹、环境监测中的数据治理问题，以及技术解决方案主义排挤结构性政策改革的风险。本文采用定性-阐释方法并结合主题内容分析，描绘了当前的研究图景，并提出了一个理解AI-生态创新融合的框架，涵盖四个层面：技术、组织、制度和生态。本文的结论是，该领域的有意义进展不仅取决于AI本身，还取决于技术专家、环境科学家、经济学家和监管者之间自觉的多学科协作。本文提出了未来研究方向，包括纵向影响研究以及衡量AI赋能创新净环境价值的框架。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":52},"该论文讨论AI与生态创新的跨学科融合，未聚焦三农、农业信息化或智慧农业等本平台主题，相关性不足。",[54,55],{"name":48,"url":45},{"name":48,"url":56},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22912614",[12],[],"TheConfluenceofAIandEco-Innovation:Shapi-3346","10.5281\u002Fzenodo.22912613",{"doi":60,"openalex_id":62,"authors":63,"venue":48,"cited_by_count":15,"oa_url":45,"card":66,"direction":72,"ingested_from":36},"W7214104704",[64],{"name":65,"orcid":9},"Saleha Javed Abbas Syed",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"综述AI与生态创新融合的多学科研究，提出四层分析框架并指出未来方向。","结构化文献综述与主题内容分析，涵盖学术、产业与政策文本。","AI正重塑生态创新逻辑，但需警惕能耗、数据治理与技术解决主义风险。","农业绿色发展与碳","可聚焦AI赋能农业碳核算与精准管理的净环境价值评估及跨学科治理框架。","智慧农业 \u002F 农业物联网","2026-09-24T23:30:09.754901Z",{"id":75,"title":76,"url":77,"summary":78,"summary_zh":79,"content":9,"source_name":80,"source_url":77,"published_at":81,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":82,"sources":84,"tags":86,"search_phrases":87,"slug":88,"view_count":15,"doi":89,"paper":90,"created_at":119},3275,"Reporting, verification, and accounting frameworks for carbon dioxide removal: Scientific integrity, permanence, and governance challenges across emerging pathways","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rechem.2026.103860","Carbon dioxide removal (CDR) is becoming an important component of long-term climate mitigation. However, its climate value depends on whether removal claims reflect measurable, durable changes in atmospheric CO₂ rather than gross carbon capture or storage alone. This review evaluates monitoring, reporting, verification, and accounting (MRV\u002FA) across biological, biochar-based, geochemical, oceanic, and engineered CDR pathways using evidence published between 2015 and 2026. The synthesis is organized around four linked functions: measuring the physical intervention, validating the net atmospheric effect, recording the resulting carbon-accounting unit, and using it in markets, corporate claims, or national inventories. The evidence shows that credible accounting requires explicit treatment of counterfactual baselines, additionality, lifecycle emissions, leakage, timing, uncertainty, storage duration, and reversal risk. Remote sensing, field sensors, geochemical measurements, lifecycle assessment, computational models, and artificial intelligence (AI) can strengthen individual stages of MRV\u002FA, but their roles and validation requirements differ. Registries improve traceability but cannot compensate for weak measurement or accounting rules. Current policy and market frameworks also remain heterogeneous in how they treat durability, liability, uncertainty, and ownership. Future MRV\u002FA systems should therefore link pathway-specific measurements with transparent validation rules, time-dependent accounting, interoperable records, environmental performance, social legitimacy, and clearly assigned long-term responsibility.","二氧化碳去除（carbon dioxide removal, CDR）正成为长期气候减缓的重要组成部分。然而，其气候价值取决于去除声明是否反映了大气中CO₂可测量、持久的实际变化，而非仅反映碳捕集或封存的毛量。本综述基于2015年至2026年间发表的证据，评估了生物类、生物炭类、地球化学类、海洋类及工程类CDR路径的监测、报告、核查与核算（MRV\u002FA）。综述围绕四个相互关联的功能展开：测量物理干预、验证净大气效应、记录由此产生的碳核算单位，以及将其用于市场、企业声明或国家清单。证据表明，可信的核算需要明确处理反事实基线、额外性、生命周期排放、泄漏、时间尺度、不确定性、封存持续时间及逆转风险。遥感、实地传感器、地球化学测量、生命周期评估、计算模型和人工智能（AI）可强化MRV\u002FA的各个阶段，但其作用和验证要求各不相同。登记簿可提高可追溯性，但无法弥补测量或核算规则的薄弱。当前政策和市场框架在处理持久性、责任、不确定性和所有权方面也仍存在差异。因此，未来的MRV\u002FA系统应将路径特异性测量与透明的验证规则、时间依赖型核算、可互操作的记录、环境绩效、社会合法性及明确分配的长期责任相衔接。","Results in Chemistry","2026-09-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":83},"该文为二氧化碳去除的MRV与核算框架综述，属气候环境科学领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[85],{"name":80,"url":77},[12],[],"Reporting,verification,andaccountingfram-3275","10.1016\u002Fj.rechem.2026.103860",{"doi":89,"openalex_id":91,"authors":92,"venue":80,"cited_by_count":15,"oa_url":113,"card":114,"direction":72,"ingested_from":36},"W7213742688",[93,96,98,100,102,104,106,109,111],{"name":94,"orcid":95},"Palanivendhan Murugadoss","https:\u002F\u002Forcid.org\u002F0000-0003-0388-1547",{"name":97,"orcid":9},"S. V. Niveditha",{"name":99,"orcid":9},"Sandeep Kumar Jain",{"name":101,"orcid":9},"Sujai Selvarajan",{"name":103,"orcid":9},"Sasmeeta Tripathy",{"name":105,"orcid":9},"Priya Parag Saxena",{"name":107,"orcid":108},"Ravikumar Jayabal","https:\u002F\u002Forcid.org\u002F0000-0001-7877-9913",{"name":110,"orcid":9},"Aseel Smerat",{"name":112,"orcid":9},"K. Kamakshi Priya","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2211715626008349\u002Fpdf",{"tldr":115,"method":116,"finding":117,"direction":70,"opportunity":118},"综述2015-2026年各类二氧化碳去除路径的监测、报告、核查与核算框架及其科学完整性问题。","综述生物、生物炭、地球化学、海洋与工程化CDR的MRV\u002FA证据，涉及遥感、传感器","可信核算须显式处理基线、额外性、泄漏、时间与逆转风险；登记系统无法弥补测量与规则缺陷。","可研究农业CDR路径的路径特异性MRV指标与时间依赖核算规则，并探索AI验证与跨登记系统互操作。","2026-09-23T23:30:13.215315Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":126,"source_url":123,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":127,"sources":129,"tags":131,"search_phrases":132,"slug":133,"view_count":15,"doi":134,"paper":135,"created_at":153},3170,"Cryospheric risks in the Himalayan region: impacts and community perceptions on snow avalanches and GLOFs in Chitral, Pakistan","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11069-026-08414-0","Abstract Glacial Lake Outburst Floods (GLOFs) and snow avalanches pose recurrent threats to mountain communities across the Himalayas, Karakoram, and Hindukush regions. Recent glacier retreat, driven by rising temperatures, has intensified GLOF risk. Remote sensing-based inventories have mapped 3044 glacial lakes in Khyber Pakhtunkhwa (KPK) and Gilgit-Baltistan (GB). This study examines the impact of GLOFs and snow avalanches on livelihoods and infrastructure in Chitral District, as well as community perceptions of these hazards. Data were collected through household surveys in 10 villages, yielding 350 responses. Descriptive analysis was conducted to assess the extent of damage caused by these events. The findings reveal that while livestock losses were relatively minor, the effects on agriculture, crops, and infrastructure were substantial. Critical infrastructure, such as irrigation systems, roads, and bridges, required repairs that took 9 months or more to complete. A paired sample t-test further highlights a gap in risk awareness. While snow avalanches are widely recognized as a serious threat, local planning and preparedness efforts often underestimate GLOFs, despite their severe consequences. The study emphasizes the importance of involving local communities in decision-making and resilience planning to address these evolving hazards effectively.","摘要 冰川湖溃决洪水（GLOFs）和雪崩对喜马拉雅、喀喇昆仑和兴都库什地区的山区社区构成反复出现的威胁。近年来，气温上升导致的冰川退缩加剧了冰川湖溃决洪水的风险。基于遥感的编目已绘制了开伯尔-普什图省（KPK）和吉尔吉特-巴尔蒂斯坦（GB）的3044个冰川湖。本研究考察了冰川湖溃决洪水和雪崩对吉德拉尔县生计和基础设施的影响，以及社区对这些灾害的认知。数据通过在10个村庄开展入户调查收集，共获得350份回复。采用描述性分析评估这些事件造成的损失程度。研究发现，虽然牲畜损失相对较小，但对农业、作物和基础设施的影响十分严重。灌溉系统、道路和桥梁等关键基础设施所需的修复耗时9个月或更长。配对样本t检验进一步揭示了风险意识方面的差距。尽管雪崩被广泛认为是严重威胁，但地方规划和备灾工作往往低估了冰川湖溃决洪水，尽管其后果十分严重。本研究强调让当地社区参与决策和韧性规划的重要性，以有效应对这些不断演变的灾害。","Natural Hazards",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":128},"该论文研究巴基斯坦喜马拉雅地区冰川湖溃决与雪崩风险，属自然灾害与山地社区研究，与三农、农业信息化、智慧农业等主题无关。",[130],{"name":126,"url":123},[12],[],"CryosphericrisksintheHimalayanregion:imp-3170","10.1007\u002Fs11069-026-08414-0",{"doi":134,"openalex_id":136,"authors":137,"venue":126,"cited_by_count":15,"oa_url":123,"card":148,"direction":33,"ingested_from":36},"W7213974324",[138,140,143,146],{"name":139,"orcid":9},"Syed ul Abrar",{"name":141,"orcid":142},"Irfan Ahmad Rana","https:\u002F\u002Forcid.org\u002F0000-0002-3157-1186",{"name":144,"orcid":145},"Shahbaz Altaf","https:\u002F\u002Forcid.org\u002F0000-0001-7846-4129",{"name":147,"orcid":9},"Muhammad Israr Siddiqui",{"tldr":149,"method":150,"finding":151,"direction":70,"opportunity":152},"研究巴基斯坦吉德拉尔地区雪崩与冰湖溃决洪水对生计和基础设施的影响及社区风险认知。","在10个村庄开展350份入户调查，用描述性统计和配对样本t检验分析。","农业、作物和基础设施受损严重，修复需9个月以上；社区低估冰湖溃决洪水风险。","可结合遥感冰湖编目与社区感知数据，构建山区农业基础设施脆弱性评估与预警模型。","2026-09-22T23:30:22.863654Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":162,"sources":164,"tags":166,"search_phrases":167,"slug":168,"view_count":15,"doi":169,"paper":170,"created_at":181},3087,"Loss-Aware Residual Learning for Imbalanced Multi-Class Diabetic Retinopathy Diagnosis","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijhpr.vol.11.no2.2026.pg1.41","Diabetic Retinopathy (DR) is a severe complication of diabetes that can lead to vision impairment or blindness, making early detection crucial. Traditional deep learning models often struggle with class imbalance in medical datasets, leading to poor performance for minority classes. This study proposes a novel deep learning model based on Residual Networks (ResNet) to address the multi class classification of DR, with a focus on mitigating class imbalance. Standard Softmax activation functions tend to favor majority classes, thereby worsening the model's performance on minority classes. To address this, the study incorporates a custom loss function, Balanced Softmax Loss, which adjusts class weights to improve the recognition of minority classes. Additionally, the model integrates advanced techniques such as Squeeze-and-Excitation (SE) blocks, learnable wavelet transforms, and multi-head attention mechanisms to enhance feature extraction and model performance. The model was trained and evaluated on the APTOS 2019 Blindness Detection dataset, achieving a micro-average accuracy of 0.83 and a macro-average accuracy of 0.73 in the 5-class classification task. In a 4-class classification task, where severe and proliferative DR were merged, the model achieved a micro-average accuracy of 0.87 and a macro-average accuracy of 0.84. The model's interpretability was further enhanced through Explainable AI (XAI) techniques such as LIME, Grad-CAM, and SHAP. The trained model was deployed as a web-based application using Flask, enabling real-time classification of retinal images. The study highlights the model's effectiveness in addressing class imbalance and its potential for early DR diagnosis, thereby enhancing clinical decision support.","糖尿病视网膜病变（Diabetic Retinopathy, DR）是糖尿病的一种严重并发症，可导致视力损害甚至失明，因此早期检测至关重要。传统的深度学习模型在处理医学数据集中的类别不平衡问题时往往表现不佳，导致少数类别的识别性能较差。本研究提出了一种基于残差网络（Residual Networks, ResNet）的新型深度学习模型，用于DR的多类别分类，重点在于缓解类别不平衡问题。标准Softmax激活函数倾向于偏向多数类别，从而加剧了模型在少数类别上的性能不足。为解决这一问题，本研究引入了一种自定义损失函数——平衡Softmax损失（Balanced Softmax Loss），通过调整类别权重来提升少数类别的识别能力。此外，该模型还集成了挤压-激励（Squeeze-and-Excitation, SE）模块、可学习小波变换和多头注意力机制等先进技术，以增强特征提取和模型性能。该模型在APTOS 2019盲ness Detection数据集上进行了训练和评估，在5分类任务中取得了0.83的微平均准确率和0.73的宏平均准确率。在将严重型和增殖型DR合并的4分类任务中，模型取得了0.87的微平均准确率和0.84的宏平均准确率。通过LIME、Grad-CAM和SHAP等可解释人工智能（Explainable AI, XAI）技术，进一步增强了模型的可解释性。训练后的模型通过Flask部署为基于Web的应用程序，实现了视网膜图像的实时分类。本研究凸显了该模型在解决类别不平衡问题方面的有效性及其在DR早期诊断中的潜力，从而增强了临床决策支持。","INTERNATIONAL JOURNAL OF HEALTH AND PHARMACEUTICAL RESEARCH","2026-09-18T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":163},"该论文研究糖尿病视网膜病变的深度学习诊断，属医学影像领域，与三农、农业信息化、智慧农业无关，不建议进入每日精选。",[165],{"name":160,"url":157},[12],[],"Loss-AwareResidualLearningforImbalancedM-3087","10.56201\u002Fijhpr.vol.11.no2.2026.pg1.41",{"doi":169,"openalex_id":171,"authors":172,"venue":160,"cited_by_count":15,"oa_url":175,"card":176,"direction":35,"ingested_from":36},"W7213544946",[173],{"name":174,"orcid":9},"Miracle Ugomma Anunobi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJHPR\u002FVOL. 11 NO. 2 2026\u002FLOSS AWARE RESIDUAL LEARNING 1-41.pdf",{"tldr":177,"method":178,"finding":179,"direction":35,"opportunity":180},"提出基于ResNet的损失感知残差学习模型，解决糖尿病视网膜病变多分类中的类别不平衡问题。","ResNet结合平衡Softmax损失、SE块、可学习小波变换和多头注意力，在A","5类分类微平均准确率0.83、宏平均0.73；合并严重与增殖期后4类分类微平均0.87、宏平均0.8","将损失感知残差学习与类别不平衡处理策略迁移至农业病害多分类诊断，提升少数类识别能力。","2026-09-21T23:30:43.782875Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":189,"sources":191,"tags":193,"search_phrases":194,"slug":195,"view_count":15,"doi":196,"paper":197,"created_at":212},3085,"Large language models in healthcare: applications, evaluation frameworks, and governance pathways — a scoping review and multidimensional framework","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffdgth.2026.1865568","Background Large language models (LLMs) are increasingly evaluated for healthcare applications spanning clinical documentation, decision support, patient communication, research assistance, and operational workflows. Despite rapid adoption interest, evidence remains heterogeneous and standardised approaches for evaluation and governance are not yet consistently applied. Objective To map healthcare applications of LLMs, synthesise reported outcomes and risks, and propose a multidimensional evaluation and governance framework oriented toward digital-health implementation. Methods A scoping review was conducted following the methodological guidance of Arksey and O'Malley, Levac et al., and the Joanna Briggs Institute, with reporting aligned to the PRISMA-ScR checklist. The protocol was prospectively registered on the Open Science Framework https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FSWP78 ). Searches were performed in PubMed\u002FMEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and the ACL Anthology, supplemented by medRxiv\u002FbioRxiv preprint searches and grey-literature scanning of WHO, FDA, and EU AI-Office guidance, covering 1 January 2019–30 April 2026. Title\u002Fabstract and full-text screening were carried out in duplicate; inter-rater agreement was Cohen's κ = 0.78. Data were extracted in duplicate using a piloted form. Synthesis followed Braun and Clarke's six-phase reflexive thematic analysis. The complete list of 78 included studies is provided in Supplementary File S4. Results Seventy-eight studies met inclusion criteria; 68% were published between 2023 and 2025. Five application domains were identified: (1) clinical documentation and summarisation; (2) clinical decision support and reasoning assistance; (3) patient communication and health-literacy support; (4) biomedical research and knowledge synthesis; and (5) administrative and operational use cases. The evidence base was dominated by benchmark and simulated-workflow studies (89%), with limited prospective workflow-embedded evaluations (11%). Reported benefits concentrated on documentation efficiency, text quality, and knowledge synthesis; safety-relevant risks included hallucinated content, omission of clinically critical information, demographic bias, privacy vulnerabilities, limited explainability, and automation bias. Studies were geographically concentrated in North America and East Asia, with limited representation from Sub-Saharan Africa, South Asia, and Latin America. Conclusions Current evidence supports cautious deployment of LLMs in selected healthcare tasks under structured oversight. Translational progress depends on prospective evaluation, standardised reporting, equity-focused audits, and lifecycle governance with continuous monitoring. The proposed five-dimensional framework (technical performance, clinical validity, equity, workflow integration, governance) coupled with a three-tier risk model is intended to support researchers and healthcare organisations in assessing readiness and implementing LLM-enabled tools responsibly.","背景 大语言模型（Large Language Models, LLMs）在医疗健康领域的应用评估日益增多，涵盖临床文档、决策支持、患者沟通、研究辅助和运营工作流程。尽管应用兴趣迅速增长，但证据仍呈现异质性，评估与治理的标准化方法尚未得到一致应用。目的 梳理大语言模型在医疗健康领域的应用，综合已报告的结局与风险，并提出面向数字健康实施的多维评估与治理框架。方法 按照Arksey和O'Malley、Levac等人以及乔安娜布里格斯研究所的方法学指导开展范围综述，报告遵循PRISMA-ScR清单。研究方案已在开放科学框架（Open Science Framework）前瞻性注册（https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FSWP78）。检索在PubMed\u002FMEDLINE、Embase、Scopus、Web of Science、IEEE Xplore、ACM Digital Library和ACL Anthology中进行，并补充检索medRxiv\u002FbioRxiv预印本以及世界卫生组织（WHO）、美国食品药品监督管理局（FDA）和欧盟人工智能办公室（EU AI-Office）指南的灰色文献，覆盖2019年1月1日至2026年4月30日。标题\u002F摘要和全文筛选由两人独立完成；评分者间一致性为Cohen's κ = 0.78。数据采用经预试验的表格由两人独立提取。综合采用Braun和Clarke的六阶段反思性主题分析法。78项纳入研究的完整列表见补充文件S4。结果 78项研究符合纳入标准；68%发表于2023年至2025年间。识别出五个应用领域：（1）临床文档与摘要生成；（2）临床决策支持与推理辅助；（3）患者沟通与健康素养支持；（4）生物医学研究与知识综合；（5）行政与运营用例。证据基础以基准测试和模拟工作流程研究为主（89%），前瞻性嵌入工作流程的评估有限（11%）。已报告的获益集中在文档效率、文本质量和知识综合方面；与安全相关的风险包括幻觉内容、临床关键信息遗漏、人口统计学偏倚、隐私漏洞、可解释性有限和自动化偏倚。研究在地理上集中于北美和东亚，撒哈拉以南非洲、南亚和拉丁美洲的代表性有限。结论 Cu","Frontiers in Digital Health",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":190},"该文为医疗领域大语言模型的范围综述，与三农、农业信息化、智慧农业等主题无关，不予入选。",[192],{"name":188,"url":185},[12],[],"Largelanguagemodelsinhealthcare:applicat-3085","10.3389\u002Ffdgth.2026.1865568",{"doi":196,"openalex_id":198,"authors":199,"venue":188,"cited_by_count":15,"oa_url":204,"card":205,"direction":211,"ingested_from":36},"W7213552002",[200,202],{"name":201,"orcid":9},"João C. Ferreira",{"name":203,"orcid":9},"Isabel Rosa","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fdigital-health\u002Farticles\u002F10.3389\u002Ffdgth.2026.1865568\u002Fpdf",{"tldr":206,"method":207,"finding":208,"direction":209,"opportunity":210},"综述医疗大模型应用，提出多维评估与治理框架。","范围综述，检索7大数据库及灰色文献，主题分析78项研究。","应用分五域，证据多为基准测试，缺前瞻性嵌入评估，风险含幻觉与偏见。","其他","医疗LLM评估治理框架可迁移至农业大模型，填补农业场景前瞻性验证空白。","数字乡村与农业信息化","2026-09-21T23:30:36.936121Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":220,"sources":222,"tags":224,"search_phrases":225,"slug":226,"view_count":15,"doi":227,"paper":228,"created_at":243},3077,"A Computer Vision Survey of the Northern San Juan","https:\u002F\u002Fdoi.org\u002F10.1017\u002Faaq.2026.10223","Abstract As computer vision becomes more popular in archaeology, it is imperative to develop best practices that balance the interpretive and analytically critical tendencies of manual survey with the systemization and efficiency promised by machine learning. Using a case study from the northern US Southwest, we demonstrate an iterative image classification approach that reflects the methodological processes that play out in field research and preserves the role of humans as nuanced decision-makers. We develop a model to identify ancestral Pueblo residential sites and use it to survey an area larger than 16,000 km 2 . The semiautomated survey identified 4,905 likely archaeological features, marking one of the largest remote archaeological surveys in North America. Results not only emphasize the value of integrated computer vision for archaeological survey and site prediction but also demonstrate a research design that capitalizes on the computational value of computer vision while maintaining active engagement by the researcher.","摘要 随着计算机视觉在考古学中日益普及，亟需制定最佳实践，以平衡人工调查的解释性与分析批判性倾向同机器学习所承诺的系统化与效率之间的关系。通过美国西南部北部的一个案例研究，我们展示了一种迭代式图像分类方法，该方法反映了田野研究中实际展开的方法论过程，并保留了人类作为细致决策者的角色。我们开发了一个模型来识别祖先普韦布洛（Pueblo）居住遗址，并用其调查了超过16,000平方公里的区域。这种半自动化调查识别出4,905个可能的考古特征，是北美规模最大的远程考古调查之一。研究结果不仅强调了集成计算机视觉在考古调查与遗址预测中的价值，还展示了一种研究设计，该设计在利用计算机视觉计算价值的同时，保持了研究者的积极参与。","American Antiquity",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":221},"考古学计算机视觉调查论文，与三农、农业信息化、智慧农业主题无关，不予入选。",[223],{"name":219,"url":216},[12],[],"AComputerVisionSurveyoftheNorthernSanJua-3077","10.1017\u002Faaq.2026.10223",{"doi":227,"openalex_id":229,"authors":230,"venue":219,"cited_by_count":15,"oa_url":237,"card":238,"direction":33,"ingested_from":36},"W7213548823",[231,234],{"name":232,"orcid":233},"Sean Field","https:\u002F\u002Forcid.org\u002F0000-0002-3144-5796",{"name":235,"orcid":236},"L. A. Dean","https:\u002F\u002Forcid.org\u002F0009-0000-7038-8176","https:\u002F\u002Fwww.cambridge.org\u002Fcore\u002Fservices\u002Faop-cambridge-core\u002Fcontent\u002Fview\u002F6BE98BA78AE0BFF7A688E579B669BB8F\u002FS0002731626102236a.pdf\u002Fdiv-class-title-a-computer-vision-survey-of-the-northern-san-juan-div.pdf",{"tldr":239,"method":240,"finding":241,"direction":33,"opportunity":242},"用迭代图像分类模型识别祖先普韦布洛居住遗址，半自动调查超1.6万平方公里区域。","迭代图像分类、计算机视觉模型，遥感影像，考古遗址识别。","识别出4905个可能考古特征，是北美最大远程考古调查之一。","可借鉴其迭代人机协同方法，用于农业遗址或耕地遥感识别与制图。","2026-09-21T23:30:25.985394Z"]