Transforming Healthcare: Artificial Intelligence (AI) Applications in Medical Imaging and Drug Response Prediction.

Q4 Biochemistry, Genetics and Molecular Biology
Genome Integrity Pub Date : 2025-01-22 eCollection Date: 2024-01-01 DOI:10.14293/genint.15.1.002
Karthik Prathaban, M Prakash Hande
{"title":"Transforming Healthcare: Artificial Intelligence (AI) Applications in Medical Imaging and Drug Response Prediction.","authors":"Karthik Prathaban, M Prakash Hande","doi":"10.14293/genint.15.1.002","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability. This commentary aims to discuss two potential use cases for AI in the field of medicine and the overarching challenges involved in their implementation.</p>","PeriodicalId":53596,"journal":{"name":"Genome Integrity","volume":"15 ","pages":"e20240002"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11752870/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Integrity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14293/genint.15.1.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0

Abstract

Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability. This commentary aims to discuss two potential use cases for AI in the field of medicine and the overarching challenges involved in their implementation.

改变医疗保健:人工智能(AI)在医学成像和药物反应预测中的应用。
人工智能(AI)为医学提供了广泛的增强。机器学习和深度学习技术在改善诊断和治疗结果方面显示出巨大的潜力,从帮助临床医生诊断医学图像到确定针对特定疾病的有效药物。尽管有预期的好处,但在临床环境中采用人工智能需要仔细考虑,特别是在数据概括和模型可解释性方面。本评论旨在讨论人工智能在医学领域的两个潜在用例,以及在实施过程中涉及的总体挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Genome Integrity
Genome Integrity Biochemistry, Genetics and Molecular Biology-Genetics
自引率
0.00%
发文量
1
×
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:604180095
Book学术官方微信