Treffer: [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].
Title:
[Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].
Authors:
Cheng H; Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.; Department of General Surgery, Guangdong Provincial People's Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), Ganzhou 341099, China., Yan H; First Clinical Medical School., Yuan Z; Department of General Surgery, Zhongshan People's Hospital, Zhongshan 528400, China., Zhuang Z; Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.; Department of General Surgery, Guangdong Provincial People's Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), Ganzhou 341099, China., Sun X; School of Chinese Medicine, Southern Medical University, Guangzhou 510515, China., Yao X; Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.; Department of General Surgery, Guangdong Provincial People's Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), Ganzhou 341099, China.; School of Medicine, South China University of Technology, Guangzhou 510641, China.
Source:
Nan fang yi ke da xue xue bao = Journal of Southern Medical University [Nan Fang Yi Ke Da Xue Xue Bao] 2026 Jan 20; Vol. 46 (1), pp. 231-238.
Publication Type:
Journal Article; Review; English Abstract
Language:
Chinese
Journal Info:
Publisher: Nanfang yi ke da xue xue bao bian ji bu Country of Publication: China NLM ID: 101266132 Publication Model: Print Cited Medium: Print ISSN: 1673-4254 (Print) Linking ISSN: 16734254 NLM ISO Abbreviation: Nan Fang Yi Ke Da Xue Xue Bao Subsets: MEDLINE
Imprint Name(s):
Original Publication: Guangzhou : Nanfang yi ke da xue xue bao bian ji bu, 2005-
MeSH Terms:
References:
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JMIR Med Inform. 2025 Sep 8;13:e76252. (PMID: 40921065)
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BMC Urol. 2025 Mar 29;25(1):64. (PMID: 40158093)
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Radiology. 2024 Apr;311(1):e232133. (PMID: 38687216)
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JCO Clin Cancer Inform. 2024 May;8:e2300122. (PMID: 38788166)
Digit Health. 2025 May 29;11:20552076251346703. (PMID: 40453047)
Endocrine. 2025 Mar;87(3):1041-1049. (PMID: 39394537)
Acad Radiol. 2025 Feb;32(2):624-633. (PMID: 39245597)
J Pathol Clin Res. 2024 Nov;10(6):e70009. (PMID: 39505569)
Clin Imaging. 2025 May;121:110455. (PMID: 40090067)
Br J Radiol. 2025 Mar 01;98(1167):368-374. (PMID: 39535870)
Endocr Pract. 2025 Jun;31(6):716-723. (PMID: 40139461)
J Biomed Inform. 2024 Sep;157:104720. (PMID: 39233209)
Front Radiol. 2024 Jul 05;4:1390774. (PMID: 39036542)
BioData Min. 2025 Jul 24;18(1):48. (PMID: 40707949)
BMC Med Inform Decis Mak. 2024 Oct 24;24(1):310. (PMID: 39444035)
Acad Radiol. 2025 Jun;32(6):3608-3617. (PMID: 39924377)
Comput Methods Programs Biomed. 2025 Oct;270:108922. (PMID: 40633400)
NPJ Digit Med. 2021 Jun 3;4(1):93. (PMID: 34083689)
Radiology. 2024 Mar;310(3):e232255. (PMID: 38470237)
Jpn J Radiol. 2025 Jan;43(1):51-55. (PMID: 39162781)
Pathol Res Pract. 2025 Oct;274:156159. (PMID: 40784087)
JMIR Form Res. 2025 Aug 19;9:e70863. (PMID: 40829145)
Liver Int. 2025 Jun;45(6):e70115. (PMID: 40347005)
Med Biol Eng Comput. 2025 Sep 25;:. (PMID: 40993406)
BMC Med Inform Decis Mak. 2024 Oct 3;24(1):283. (PMID: 39363322)
Ultrasound Med Biol. 2024 Nov;50(11):1697-1703. (PMID: 39138026)
JCO Clin Cancer Inform. 2024 Dec;8:e2400126. (PMID: 39661914)
Stud Health Technol Inform. 2025 Jun 26;328:56-60. (PMID: 40588880)
Liver Int. 2024 Jul;44(7):1578-1587. (PMID: 38651924)
J Med Syst. 2025 Sep 24;49(1):118. (PMID: 40991110)
Med Oral Patol Oral Cir Bucal. 2025 Mar 01;30(2):e224-e231. (PMID: 39864088)
J Med Internet Res. 2025 Jun 11;27:e72638. (PMID: 40499132)
Int J Med Inform. 2025 Nov;203:106013. (PMID: 40554367)
Neuroradiology. 2024 Jan;66(1):73-79. (PMID: 37994939)
Radiol Med. 2025 Jul;130(7):1013-1023. (PMID: 40232657)
Knee. 2025 Aug;55:153-160. (PMID: 40311171)
Radiology. 2024 Jul;312(1):e232640. (PMID: 39041936)
Acad Radiol. 2025 May;32(5):2385-2391. (PMID: 39658474)
Cell Rep Med. 2025 Mar 18;6(3):101988. (PMID: 40043704)
Eur Radiol. 2025 Apr;35(4):1938-1947. (PMID: 39198333)
J Med Screen. 2025 Sep;32(3):172-175. (PMID: 40259576)
Eur J Radiol. 2024 Jun;175:111458. (PMID: 38613868)
Front Med (Lausanne). 2025 Aug 22;12:1618858. (PMID: 40917864)
AJR Am J Roentgenol. 2024 Dec;223(6):e2431696. (PMID: 39230409)
Nat Med. 2025 Feb;31(2):618-626. (PMID: 39779928)
JMIR Med Inform. 2024 Dec 20;12:e67056. (PMID: 39705675)
JMIR Med Inform. 2025 Sep 8;13:e76252. (PMID: 40921065)
Front Med (Lausanne). 2025 Jan 23;11:1507203. (PMID: 39917264)
BMC Urol. 2025 Mar 29;25(1):64. (PMID: 40158093)
Jpn J Radiol. 2025 Apr;43(4):706-712. (PMID: 39585559)
Radiology. 2024 Apr;311(1):e232133. (PMID: 38687216)
Quant Imaging Med Surg. 2024 Feb 1;14(2):1602-1615. (PMID: 38415150)
Cancers (Basel). 2025 Jun 10;17(12):. (PMID: 40563585)
Tomography. 2025 Jun 17;11(6):. (PMID: 40560015)
JCO Clin Cancer Inform. 2024 May;8:e2300122. (PMID: 38788166)
Digit Health. 2025 May 29;11:20552076251346703. (PMID: 40453047)
Endocrine. 2025 Mar;87(3):1041-1049. (PMID: 39394537)
Acad Radiol. 2025 Feb;32(2):624-633. (PMID: 39245597)
J Pathol Clin Res. 2024 Nov;10(6):e70009. (PMID: 39505569)
Clin Imaging. 2025 May;121:110455. (PMID: 40090067)
Br J Radiol. 2025 Mar 01;98(1167):368-374. (PMID: 39535870)
Endocr Pract. 2025 Jun;31(6):716-723. (PMID: 40139461)
J Biomed Inform. 2024 Sep;157:104720. (PMID: 39233209)
Front Radiol. 2024 Jul 05;4:1390774. (PMID: 39036542)
BioData Min. 2025 Jul 24;18(1):48. (PMID: 40707949)
BMC Med Inform Decis Mak. 2024 Oct 24;24(1):310. (PMID: 39444035)
Acad Radiol. 2025 Jun;32(6):3608-3617. (PMID: 39924377)
Comput Methods Programs Biomed. 2025 Oct;270:108922. (PMID: 40633400)
NPJ Digit Med. 2021 Jun 3;4(1):93. (PMID: 34083689)
Grant Information:
82260501 and 82274387 National Natural Science Foundation of China
Contributed Indexing:
Keywords: artificial intelligence; cancer diagnosis; large language models; medical imaging; pathology
Local Abstract: [Publisher, Chinese] 大语言模型（LLMs）作为新兴人工智能技术，凭借其优异的文字与图像处理能力，为医疗领域智能化变革提供核心支撑，显著提升临床工作效率与质量。本文系统梳理LLMs在癌症诊断领域的应用现状、技术特点及发展方向，重点聚焦两大核心场景:一是影像报告、病理报告、综合病例报告等文字报告的自动化分析与解读;二是融合文本与医学影像的多模态数据诊断。研究发现，LLMs在癌症诊断中的综合能力已可媲美普通住院医师，但在专业化诊断与精准化判断方面仍存在明显短板;同时，LLMs展现出“小参数模型适配基层场景”“多语言报告分析泛用性差异”等应用层面特征。未来需进一步开发专业化、实用化的医疗专用LLMs，通过优化微调策略、构建高质量中文医疗数据集、整合视觉语言模型等方式，推动其临床落地并弥合医疗资源差距。.
Local Abstract: [Publisher, Chinese] 大语言模型（LLMs）作为新兴人工智能技术，凭借其优异的文字与图像处理能力，为医疗领域智能化变革提供核心支撑，显著提升临床工作效率与质量。本文系统梳理LLMs在癌症诊断领域的应用现状、技术特点及发展方向，重点聚焦两大核心场景:一是影像报告、病理报告、综合病例报告等文字报告的自动化分析与解读;二是融合文本与医学影像的多模态数据诊断。研究发现，LLMs在癌症诊断中的综合能力已可媲美普通住院医师，但在专业化诊断与精准化判断方面仍存在明显短板;同时，LLMs展现出“小参数模型适配基层场景”“多语言报告分析泛用性差异”等应用层面特征。未来需进一步开发专业化、实用化的医疗专用LLMs，通过优化微调策略、构建高质量中文医疗数据集、整合视觉语言模型等方式，推动其临床落地并弥合医疗资源差距。.
Entry Date(s):
Date Created: 20260116 Date Completed: 20260116 Latest Revision: 20260121
Update Code:
20260121
PubMed Central ID:
PMC12809042
DOI:
10.12122/j.issn.1673-4254.2026.01.25
PMID:
41540710
Database:
MEDLINE
Weitere Informationen
Large language models (LLMs) are emerging artificial intelligence technologies with strong text and image processing capabilities, offering critical support for the intelligent transformation of healthcare and improving clinical efficiency and quality. This review summarizes the current applications, technical features, and future directions of LLMs in cancer diagnosis, focusing on two key scenarios: automated analysis of textual reports (e.g., imaging, pathology, and case summaries) and multimodal diagnosis combining text and medical images. Findings show that LLMs now perform at a level comparable to general resident physicians in cancer diagnosis but are still incapable of making specialized and precise judgments. They also exhibit application-specific traits, such as parameter-efficient models adapted for grassroots-level scenario and divergent versatility in multilingual report analysis. Future efforts should prioritize developing specialized, practical medical LLMs through optimized fine-tuning strategies, construction of high-quality Chinese medical datasets, and integration with vision-language models to promote the clinical application of these models and increase the accessibility of healthcare resources.