联合学习在神经病学中的应用：在中风、精神病、阿尔茨海默病和帕金森病中的应用。

Q3 Medicine

Recenti progressi in medicina Pub Date : 2025-10-01 DOI:10.1701/4573.45790

Cosimo Guerra, Rosario De Feo

{"title":"联合学习在神经病学中的应用：在中风、精神病、阿尔茨海默病和帕金森病中的应用。","authors":"Cosimo Guerra, Rosario De Feo","doi":"10.1701/4573.45790","DOIUrl":null,"url":null,"abstract":"AI is transforming neurology, providing powerful diagnostic and therapeutic tools, yet handling sensitive clinical data involves substantial privacy risks. Federated Learning addresses these issues by training models locally within hospitals and sharing only their weights or gradients for final training, achieving similar or superior performance compared to centralized models in Stroke, Alzheimer's, and Parkinson's disease.","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"591-592"},"PeriodicalIF":0.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Federated Learning in neurologia: applicazioni nello stroke, malattia di Alzheimer e di Parkinson.\",\"authors\":\"Cosimo Guerra, Rosario De Feo\",\"doi\":\"10.1701/4573.45790\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AI is transforming neurology, providing powerful diagnostic and therapeutic tools, yet handling sensitive clinical data involves substantial privacy risks. Federated Learning addresses these issues by training models locally within hospitals and sharing only their weights or gradients for final training, achieving similar or superior performance compared to centralized models in Stroke, Alzheimer's, and Parkinson's disease.\",\"PeriodicalId\":20887,\"journal\":{\"name\":\"Recenti progressi in medicina\",\"volume\":\"116 10\",\"pages\":\"591-592\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Recenti progressi in medicina\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1701/4573.45790\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Recenti progressi in medicina","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1701/4573.45790","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}

引用次数: 0

摘要

人工智能正在改变神经病学，提供强大的诊断和治疗工具，但处理敏感的临床数据涉及巨大的隐私风险。联邦学习解决了这些问题，方法是在医院内部本地训练模型，并在最终训练中只共享它们的权重或梯度，在中风、阿尔茨海默病和帕金森病方面，与集中式模型相比，实现了类似或更好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Federated Learning in neurologia: applicazioni nello stroke, malattia di Alzheimer e di Parkinson.

AI is transforming neurology, providing powerful diagnostic and therapeutic tools, yet handling sensitive clinical data involves substantial privacy risks. Federated Learning addresses these issues by training models locally within hospitals and sharing only their weights or gradients for final training, achieving similar or superior performance compared to centralized models in Stroke, Alzheimer's, and Parkinson's disease.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Recenti progressi in medicina Medicine-Medicine (all)

CiteScore

0.90

自引率

0.00%

发文量

143

期刊介绍： Giunta ormai al sessantesimo anno, Recenti Progressi in Medicina continua a costituire un sicuro punto di riferimento ed uno strumento di lavoro fondamentale per l"ampliamento dell"orizzonte culturale del medico italiano. Recenti Progressi in Medicina è una rivista di medicina interna. Ciò significa il recupero di un"ottica globale e integrata, idonea ad evitare sia i particolarismi della informazione specialistica sia la frammentazione di quella generalista.