Revolutionising Osseous Biopsy: The Impact of Artificial Intelligence in the Era of Personalised Medicine.

IF 1.8 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Amanda Isaac, Michail E Klontzas, Danoob Dalili, Asli Irmak Akdogan, Mohamed Fawzi, Giuseppe Gugliemi, Dimitrios Filippiadis
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引用次数: 0

Abstract

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalised medicine. This paper explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalised medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation and generative models. We explore their roles in tumour board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalisability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

在快速发展的医疗环境中,人工智能(AI)正在改变诊断技术和个性化医疗。这在骨活检中也可见。人工智能在放射组学、组织病理学、预测建模、活检导航和跨学科交流中的应用正在重塑骨活检的进行和解释方式。我们提供了一个简短的回顾人工智能在图像引导骨肿瘤活检(原发性和继发性)和标本处理,在个性化医疗时代。本文探讨了人工智能在提高诊断准确性、提高活检安全性以及在骨病变活检中实现更精确的靶向方面的作用,最终为个性化医疗提供更好的患者结果。我们深入研究了应用于骨活检的各种人工智能技术,如传统的机器学习、深度学习、放射组学、模拟和生成模型。我们将探讨他们在肿瘤委员会会议中的作用,以及临床医生、放射科医生和病理学家之间的交流。此外,我们还考察了与人工智能在骨活检程序中的整合相关的伦理考虑、技术限制,并深入研究了人工智能医疗保健中的健康公平、普遍性、部署问题和报销挑战。最后,我们探讨了潜在的未来发展,并提供了与骨活检相关的开源人工智能工具和算法列表,我们将其包括在内,以鼓励进一步的讨论和研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
British Journal of Radiology
British Journal of Radiology 医学-核医学
CiteScore
5.30
自引率
3.80%
发文量
330
审稿时长
2-4 weeks
期刊介绍: BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences. Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896. Quick Facts: - 2015 Impact Factor – 1.840 - Receipt to first decision – average of 6 weeks - Acceptance to online publication – average of 3 weeks - ISSN: 0007-1285 - eISSN: 1748-880X Open Access option
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