直觉-人工智能治疗互补性。

IF 2.4 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Cancer Biotherapy and Radiopharmaceuticals Pub Date : 2025-04-01 Epub Date: 2025-02-20 DOI:10.1089/cbr.2025.0021
J Harvey Turner
{"title":"直觉-人工智能治疗互补性。","authors":"J Harvey Turner","doi":"10.1089/cbr.2025.0021","DOIUrl":null,"url":null,"abstract":"<p><p>Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where radiomics can consistently outperform human analysis and reporting capability, and do it faster, with greater accuracy and reliability. However, claims made for generative AI in respect of decision-making in the clinical practice of theranostic nuclear medicine are highly contentious. Statistical computer algorithms cannot emulate human emotion, reason, instinct, intuition, or empathy. AI simulates intelligence without possessing it. AI has no understanding of the meaning of its outputs. The unique statistical probability attributes of large language models of AI must be complemented by the innate human intuitive capabilities of nuclear physicians who accept the responsibility and accountability for direct clinical care of each individual patient referred for theranostic management of specified cancers. Complementarity envisions synergistic engagement with AI radiomics, genomics, radiobiology, dosimetry, and data collation from multidimensional sources, including the electronic medical record, to enable the nuclear physician to spend informed face time with their patient. Together with physician discernment, application of the technical insights from AI will facilitate optimal formulation of a personalized precision theranostic strategy for empathic, efficacious, targeted treatment of the patient with cancer in accordance with their wishes.</p>","PeriodicalId":55277,"journal":{"name":"Cancer Biotherapy and Radiopharmaceuticals","volume":" ","pages":"153-160"},"PeriodicalIF":2.4000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intuitive Human-Artificial Intelligence Theranostic Complementarity.\",\"authors\":\"J Harvey Turner\",\"doi\":\"10.1089/cbr.2025.0021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where radiomics can consistently outperform human analysis and reporting capability, and do it faster, with greater accuracy and reliability. However, claims made for generative AI in respect of decision-making in the clinical practice of theranostic nuclear medicine are highly contentious. Statistical computer algorithms cannot emulate human emotion, reason, instinct, intuition, or empathy. AI simulates intelligence without possessing it. AI has no understanding of the meaning of its outputs. The unique statistical probability attributes of large language models of AI must be complemented by the innate human intuitive capabilities of nuclear physicians who accept the responsibility and accountability for direct clinical care of each individual patient referred for theranostic management of specified cancers. Complementarity envisions synergistic engagement with AI radiomics, genomics, radiobiology, dosimetry, and data collation from multidimensional sources, including the electronic medical record, to enable the nuclear physician to spend informed face time with their patient. Together with physician discernment, application of the technical insights from AI will facilitate optimal formulation of a personalized precision theranostic strategy for empathic, efficacious, targeted treatment of the patient with cancer in accordance with their wishes.</p>\",\"PeriodicalId\":55277,\"journal\":{\"name\":\"Cancer Biotherapy and Radiopharmaceuticals\",\"volume\":\" \",\"pages\":\"153-160\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Biotherapy and Radiopharmaceuticals\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/cbr.2025.0021\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Biotherapy and Radiopharmaceuticals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/cbr.2025.0021","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/20 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

深度学习人工智能(AI)算法有望将放射学和核医学的诊断成像专家纳入其中,在这些领域,放射组学可以不断超越人类的分析和报告能力,并且速度更快,准确性和可靠性更高。然而,在治疗性核医学的临床实践中,关于生成式人工智能在决策方面的主张是极具争议的。统计计算机算法无法模拟人类的情感、理性、本能、直觉或同理心。人工智能在不拥有智能的情况下模拟智能。人工智能无法理解其输出的含义。人工智能的大型语言模型的独特统计概率属性必须与核医生天生的人类直觉能力相辅相成,核医生接受责任和责任,为每一个接受特定癌症治疗管理的患者提供直接的临床护理。互补性设想与人工智能放射组学、基因组学、放射生物学、剂量学和来自多维来源(包括电子病历)的数据整理进行协同合作,使核医生能够与患者面对面交流。结合医生的洞察力,人工智能技术见解的应用将促进个性化精确治疗策略的优化制定,从而根据癌症患者的意愿对其进行移情、有效、有针对性的治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intuitive Human-Artificial Intelligence Theranostic Complementarity.

Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where radiomics can consistently outperform human analysis and reporting capability, and do it faster, with greater accuracy and reliability. However, claims made for generative AI in respect of decision-making in the clinical practice of theranostic nuclear medicine are highly contentious. Statistical computer algorithms cannot emulate human emotion, reason, instinct, intuition, or empathy. AI simulates intelligence without possessing it. AI has no understanding of the meaning of its outputs. The unique statistical probability attributes of large language models of AI must be complemented by the innate human intuitive capabilities of nuclear physicians who accept the responsibility and accountability for direct clinical care of each individual patient referred for theranostic management of specified cancers. Complementarity envisions synergistic engagement with AI radiomics, genomics, radiobiology, dosimetry, and data collation from multidimensional sources, including the electronic medical record, to enable the nuclear physician to spend informed face time with their patient. Together with physician discernment, application of the technical insights from AI will facilitate optimal formulation of a personalized precision theranostic strategy for empathic, efficacious, targeted treatment of the patient with cancer in accordance with their wishes.

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
CiteScore
7.80
自引率
2.90%
发文量
87
审稿时长
3 months
期刊介绍: Cancer Biotherapy and Radiopharmaceuticals is the established peer-reviewed journal, with over 25 years of cutting-edge content on innovative therapeutic investigations to ultimately improve cancer management. It is the only journal with the specific focus of cancer biotherapy and is inclusive of monoclonal antibodies, cytokine therapy, cancer gene therapy, cell-based therapies, and other forms of immunotherapies. The Journal includes extensive reporting on advancements in radioimmunotherapy, and the use of radiopharmaceuticals and radiolabeled peptides for the development of new cancer treatments.
×
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