Seminars in Neurology最新文献_第7页

Federated Learning in Neurology: Bridging Data Privacy and Artificial Intelligence for Brain Health. 神经学中的联合学习：为大脑健康架起数据隐私和人工智能的桥梁。

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1055/a-2769-6752

Sahar Soltanieh, Farzad Khalvati, E Ann Yeh

{"title":"Federated Learning in Neurology: Bridging Data Privacy and Artificial Intelligence for Brain Health.","authors":"Sahar Soltanieh, Farzad Khalvati, E Ann Yeh","doi":"10.1055/a-2769-6752","DOIUrl":"10.1055/a-2769-6752","url":null,"abstract":"Neurological disorders affect hundreds of millions globally, yet translating artificial intelligence (AI) advances into clinical practice remains challenging due to fragmented, privacy-sensitive datasets. Federated learning (FL) has emerged as a promising paradigm, enabling collaborative model training across institutions without sharing raw patient data. This review synthesizes FL applications in neurology from 2020 to 2025, spanning neuroimaging, electrophysiology, and electronic health records. We analyze real-world deployments, highlight algorithmic trends, and discuss technical, regulatory, and organizational barriers to clinical translation. While FL demonstrates feasibility in tasks such as brain tumor segmentation, multiple sclerosis lesion detection, and electronic health record-based predictive modeling, verified clinical implementations remain scarce. We outline strategies to enhance adoption, including privacy-preserving techniques, standardized infrastructures, domain-adaptive algorithms, and cross-disciplinary collaboration. By bridging technical innovation with regulatory compliance and operational scalability, FL holds significant potential to advance precision neurology while safeguarding patient privacy.","PeriodicalId":49544,"journal":{"name":"Seminars in Neurology","volume":" ","pages":"38-48"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence and Ocular Imaging in the Evaluation of Neurologic Disorders: The New Era of Neuro-Oculomics? 人工智能和眼成像在神经疾病评估中的应用：神经显微镜的新时代？

IF 2.2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2026-02-02 DOI: 10.1055/a-2792-8597

Fiona Costello, Gemma C Norman

引用次数: 0

Advancing Neurology in the Age of Artificial Intelligence. 在人工智能时代推进神经学。

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2026-02-16 DOI: 10.1055/a-2798-4276

Scott N Grossman, Rachel C Kenney

引用次数: 0

Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias. 人工智能在阿尔茨海默病及相关痴呆中的临床应用综述。

IF 2.2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1055/a-2744-9871

Andrew G Breithaupt, Alice Tang, Emily W Paolillo, Merna Bibars, Erik C B Johnson, Rowan Saloner, Katherine L Possin, Charles C Windon, Tanisha G Hill-Jarrett, Joseph Giorgio, Andreas M Rauschecker, Hyeokhyen Kwon, Jet M J Vonk, Pedro Pinheiro-Chagas

{"title":"Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.","authors":"Andrew G Breithaupt, Alice Tang, Emily W Paolillo, Merna Bibars, Erik C B Johnson, Rowan Saloner, Katherine L Possin, Charles C Windon, Tanisha G Hill-Jarrett, Joseph Giorgio, Andreas M Rauschecker, Hyeokhyen Kwon, Jet M J Vonk, Pedro Pinheiro-Chagas","doi":"10.1055/a-2744-9871","DOIUrl":"10.1055/a-2744-9871","url":null,"abstract":"As the U.S. population ages, Alzheimer's disease and related dementias (ADRD) cases are increasing, resulting in long wait times for specialist care. We review state-of-the-art artificial intelligence (AI) applications in ADRD care, from streamlining clinical diagnosis to pioneering novel digital biomarkers. Near-term AI applications include neuroimaging interpretation, conversational agents for patient interviews, and digital cognitive assessments. Large language models show promise as collaborative partners, helping clinicians interpret complex data while supporting patients and caregivers. Emerging digital biomarkers-speech analysis, passive monitoring through wearable devices, electronic health record analysis, and multiomics-offer potential for continuous monitoring to detect cognitive decline years before traditional assessments. Despite the acceleration of AI innovation, most of these systems are inaccessible in clinical practice. Implementation bottlenecks include limited external validation, technical challenges, model biases, infrastructure, and regulatory requirements. This review aims to help neurologists navigate this rapidly evolving AI landscape and prepare for implementation in ADRD care.","PeriodicalId":49544,"journal":{"name":"Seminars in Neurology","volume":" ","pages":"105-116"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145642290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scott N. Grossman, MD, and Rachel C. Kenney, PhD. 医学博士斯科特·格罗斯曼和瑞秋·肯尼博士。

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2026-02-13 DOI: 10.1055/a-2799-1331

David M Greer

引用次数: 0

Artificial Intelligence in Stroke Imaging: A Review of Current Applications and Limitations. 人工智能在脑卒中成像中的应用及局限性

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-08-14 DOI: 10.1055/a-2683-6482

Peter I Kamel, Max Wintermark

引用次数: 0

Artificial Intelligence-Enabled Devices in Neurology: Mapping the Present and Future. 神经学中的人工智能设备：映射现在和未来。

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1055/a-2772-7189

Ashwin Amurthur, Davis J McCarthy, Lee H Schwamm, James M Hillis

引用次数: 0

Artificial Intelligence in Neurology and Stroke Education: Current Applications and Future Directions. 神经病学和中风教育中的人工智能：当前应用和未来方向。

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-10-01 DOI: 10.1055/a-2713-6622

Braydon Dymm, Abdullah Khalid

引用次数: 0

AI Prompt Engineering for Neurologists and Trainees. 神经学家和实习生的人工智能提示工程。

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-11-11 DOI: 10.1055/a-2742-2349

Valdery Moura Junior, Peter Hadar, Shawn Murphy, Lidia M V R Moura

{"title":"AI Prompt Engineering for Neurologists and Trainees.","authors":"Valdery Moura Junior, Peter Hadar, Shawn Murphy, Lidia M V R Moura","doi":"10.1055/a-2742-2349","DOIUrl":"10.1055/a-2742-2349","url":null,"abstract":"Large language models (LLMs) have transformative potential in neurology, impacting clinical decision-making, medical training, and research. Prompt engineering, the strategic design of inputs to optimize LLM performance, is essential for neurologists and trainees seeking to effectively integrate these powerful tools into practice. Carefully crafted prompts enable LLMs to summarize complex patient narratives, generate differential diagnoses, and support patient education. In training, structured prompts enhance diagnostic reasoning, board preparation, and interactive case-based learning. Neurological research also benefits, with LLMs aiding in data extraction, computed phenotype generation, and literature synthesis. Despite their promise, challenges remain, including hallucinations, data bias, privacy concerns, and regulatory complexities. This review synthesizes current advances and highlights best practices, including two structured prompt engineering frameworks tailored to neurology: Role-Task-Format (RTF) for routine use and our newly developed BRAIN (Background, Role, Aim, Instructions, Next steps) for complex tasks. We offer practical guidance to maximize accuracy, safety, and equity in LLM outputs, ensuring reliable support for neurologists and trainees.","PeriodicalId":49544,"journal":{"name":"Seminars in Neurology","volume":" ","pages":"16-25"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Operationalizing AI in Stroke Alerts: Balancing Sensitivity and Specificity in Predicting Acute Cerebrovascular Disease. 在脑卒中预警中应用人工智能：平衡急性脑血管疾病预测的敏感性和特异性

IF 2 4区医学

Seminars in Neurology Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1055/a-2769-6703

Margaret H Downes, Asala N Erekat, Benjamin R Kummer

{"title":"Operationalizing AI in Stroke Alerts: Balancing Sensitivity and Specificity in Predicting Acute Cerebrovascular Disease.","authors":"Margaret H Downes, Asala N Erekat, Benjamin R Kummer","doi":"10.1055/a-2769-6703","DOIUrl":"10.1055/a-2769-6703","url":null,"abstract":"Acute stroke alerts are frequently triggered by conditions unrelated to cerebrovascular disease, resulting in false positives that burden clinical teams and contribute to diagnostic ambiguity. At a large academic center, we developed ScanNER v2, a machine learning (ML) model based on large-language models (LLMs) and structured clinical data to predict the presence of acute cerebrovascular disease (ACD) in approximately 16,000 stroke alerts occurring over 10 years with an area under the receiver-operating curve and F1 score of 0.72 and overall positive predictive value of 0.68. In this perspective, we outline a practical framework for operationalizing this model within hospital-based stroke systems. We first describe our health-system experience developing and validating an AI-enabled pipeline, named \"ScanNER 2,\" then take the point of view of two implementation angles (high sensitivity and high specificity), outlining the operational and clinical tradeoffs for each approach. We also highlight challenges related to implementation, clinical governance, workflow integration, and equity, emphasizing guardrails required for responsible deployment. As stroke centers increasingly adopt AI-assisted tools, this type of thought experiment is essential to ensure that such ML-based innovations effectively enhance the core mission of delivering timely, high-quality acute stroke care.","PeriodicalId":49544,"journal":{"name":"Seminars in Neurology","volume":" ","pages":"92-95"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0