联合学习的医学应用调查。

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2024-01-01 Epub Date: 2024-01-31 DOI:10.4258/hir.2024.30.1.3
Geunho Choi, Won Chul Cha, Se Uk Lee, Soo-Yong Shin
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引用次数: 0

摘要

目的:医学人工智能(AI)最近引起了广泛关注。然而,由于隐私保护法规的限制,训练医学人工智能模型具有挑战性。在提出的解决方案中,联合学习(FL)脱颖而出。联合学习只涉及传输模型参数,而不共享原始数据,因此特别适用于对数据隐私要求极高的医疗领域。本研究回顾了联合学习在医疗领域的应用:我们在 Google Scholar 和 PubMed 上以 "联合学习 "为关键词,结合 "医疗"、"保健 "或 "临床 "进行了文献检索。在审阅了标题和摘要后,我们选择了 58 篇论文进行分析。我们根据所使用的数据类型、目标疾病、开放数据集的使用、FL 的本地模型以及神经网络模型对这些 FL 研究进行了分类。我们还研究了与异质性和安全性相关的问题:在所调查的 FL 研究中,最常用的数据类型是图像数据,研究最多的目标疾病是癌症和 COVID-19。大多数研究都使用了开放数据集。此外,72%的FL文章涉及异质性问题,50%的文章讨论了安全问题:医学领域的 FL 研究似乎还处于早期阶段,大多数研究使用开放数据,并侧重于特定数据类型和疾病,以达到性能验证的目的。尽管如此,医学 FL 研究预计将越来越多地得到应用,并成为多机构研究的重要组成部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey of Medical Applications of Federated Learning.

Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.

Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.

Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.

Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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