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人工智能在肿瘤委员会中的作用:外科医生、肿瘤内科医生和放射肿瘤医生的观点。

The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.

机构信息

Department of Precision Medicine, University of Campania "L. Vanvitelli", 80131 Naples, Italy.

Unit of Medical Oncology 2, Azienda Ospedaliera Universitaria Pisana, 56126 Pisa, Italy.

出版信息

Curr Oncol. 2024 Aug 27;31(9):4984-5007. doi: 10.3390/curroncol31090369.


DOI:10.3390/curroncol31090369
PMID:39329997
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11431448/
Abstract

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients-surgeons, medical oncologists, and radiation oncologists-on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.

摘要

多学科肿瘤委员会(MTB)的整合对于提供最先进的癌症治疗至关重要,通过多样化的专家团队协作,促进了诊断和管理。尽管在个性化患者护理和改善结果方面有明显的益处,但由于癌症发病率的上升和财务限制对 MTB 造成的负担不断增加,需要创新的解决方案。人工智能(AI)在医学领域的出现为支持临床决策提供了一个有前途的途径。本综述探讨了专门从事癌症患者护理的临床医生(外科医生、肿瘤内科医生和放射肿瘤医生)对 MTB 中 AI 应用的看法。此外,还研究了 AI 在涉及癌症诊断和治疗的各种临床专业中的作用。通过分析潜在的优势和挑战,本研究强调了 AI 如何增强多学科讨论和优化治疗计划。研究结果强调了 AI 可能在提高肿瘤学护理水平和维持 MTB 效率方面发挥的变革作用,以应对日益增长的临床需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/2683ef2538c2/curroncol-31-00369-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/f0cbc0e3c4a2/curroncol-31-00369-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/ff081cced28c/curroncol-31-00369-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/2683ef2538c2/curroncol-31-00369-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/f0cbc0e3c4a2/curroncol-31-00369-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/ff081cced28c/curroncol-31-00369-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e22b/11431448/2683ef2538c2/curroncol-31-00369-g003.jpg

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Multimodal integration strategies for clinical application in oncology.

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[2]
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Urologie. 2025-7-14

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Artificial intelligence in cancer pathology: Applications, challenges, and future directions.

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[5]
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[6]
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[7]
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Cancers (Basel). 2025-1-28

[8]
Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards.

J Clin Med. 2025-1-10

[9]
Progress and Challenges in Integrating Nutritional Care into Oncology Practice: Results from a National Survey on Behalf of the NutriOnc Research Group.

Nutrients. 2025-1-5

本文引用的文献

[1]
Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients.

J Clin Med. 2024-7-25

[2]
Evolution of publicly available large language models for complex decision-making in breast cancer care.

Arch Gynecol Obstet. 2024-7

[3]
Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning.

Clin Exp Med. 2024-4-10

[4]
Current Trends and Beyond Conventional Approaches: Advancements in Breast Cancer Surgery through Three-Dimensional Imaging, Virtual Reality, Augmented Reality, and the Emerging Metaverse.

J Clin Med. 2024-2-5

[5]
Challenging ChatGPT 3.5 in Senology-An Assessment of Concordance with Breast Cancer Tumor Board Decision Making.

J Pers Med. 2023-10-16

[6]
The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study.

J Gastrointest Cancer. 2024-3

[7]
Concordance of a decision algorithm and multidisciplinary team meetings for patients with liver cancer-a study protocol for a randomized controlled trial.

Trials. 2023-9-9

[8]
The prognostic value of radiomic features in liver-limited metastatic colorectal cancer patients from the TRIBE2 study.

Future Oncol. 2023-7

[9]
Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases.

Arch Gynecol Obstet. 2023-12

[10]
Artificial Intelligence Applied to clinical trials: opportunities and challenges.

Health Technol (Berl). 2023

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