医学中的多模态人工智能。

IF 3.2 Q1 UROLOGY & NEPHROLOGY
Conor S Judge, Finn Krewer, Martin J O'Donnell, Lisa Kiely, Donal Sexton, Graham W Taylor, Joshua August Skorburg, Bryan Tripp
{"title":"医学中的多模态人工智能。","authors":"Conor S Judge, Finn Krewer, Martin J O'Donnell, Lisa Kiely, Donal Sexton, Graham W Taylor, Joshua August Skorburg, Bryan Tripp","doi":"10.34067/KID.0000000000000556","DOIUrl":null,"url":null,"abstract":"<p><p>Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges.</p>","PeriodicalId":17882,"journal":{"name":"Kidney360","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multimodal Artificial Intelligence in Medicine.\",\"authors\":\"Conor S Judge, Finn Krewer, Martin J O'Donnell, Lisa Kiely, Donal Sexton, Graham W Taylor, Joshua August Skorburg, Bryan Tripp\",\"doi\":\"10.34067/KID.0000000000000556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges.</p>\",\"PeriodicalId\":17882,\"journal\":{\"name\":\"Kidney360\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Kidney360\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34067/KID.0000000000000556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kidney360","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34067/KID.0000000000000556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

被批准用于临床的传统医疗人工智能模型仅限于图像等单模态数据,这限制了其在复杂的多模态医疗诊断和治疗环境中的适用性。医疗领域的多模态变换器模型可以有效处理和解释文本、图像和结构化数据等多种数据形式。它们在 USLME 题库等标准基准上的表现令人印象深刻,并随着规模的扩大而不断改进。然而,采用这些先进的人工智能模型并非没有挑战。虽然像变形金刚这样的多模态深度学习模型为医疗保健领域带来了充满希望的进步,但整合这些模型需要仔细考虑随之而来的伦理和环境挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Artificial Intelligence in Medicine.

Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges.

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
Kidney360
Kidney360 UROLOGY & NEPHROLOGY-
CiteScore
3.90
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信