Suppr超能文献

肿瘤学中的人工智能,及其范围和未来前景,特别提及放射肿瘤学。

Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology.

作者信息

Rattan Rajit, Kataria Tejinder, Banerjee Susovan, Goyal Shikha, Gupta Deepak, Pandita Akshi, Bisht Shyam, Narang Kushal, Mishra Saumya Ranjan

机构信息

Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.

Department of Dermatology, P. N. Behl Skin Institute, New Delhi, India.

出版信息

BJR Open. 2019 May 13;1(1):20180031. doi: 10.1259/bjro.20180031. eCollection 2019.

Abstract

OBJECTIVE

Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it.

METHODS AND MATERIALS

Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made.

RESULTS

The article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it's the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future.

CONCLUSION

AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians.

摘要

目的

人工智能(AI)似乎正在弥合数据获取与有意义的解读之间的差距。这些方法已展现出卓越的能力,超越了迄今为止大多数分类和回归方法,并且能够自动学习最适合手头任务的数据表示形式,并将其呈现出来以实现更好的相关性。本文旨在促使放射肿瘤学从业者了解AI的潜在作用所在以及利用它还能进一步实现什么。

方法和材料

检索了当代文献,对现有文献进行了分类,并尝试撰写一篇全面的非系统性综述。

结果

本文探讨了肿瘤学的各个领域,尤其是放射肿瘤学领域中基于AI所开展的工作。无论是筛查方式、诊断还是预后分析,AI都能更准确地确定患者的结果和生存率。放射肿瘤学中的各种步骤和方案现在都在使用基于AI的方法,例如在放疗计划、分割和实施步骤中。AI在卫生部门所有平台上的应用可能会在不久的将来带来更精细和个性化的医疗。

结论

借助机器学习和人工神经网络的AI为肿瘤学家所面临的问题提供了更快、更准确的解决方案。AI 的应用可能会呈指数级增长。然而,鉴于在患者、疾病及其自然史方面的人口统计学差异以及有关AI被操纵的报道,最终责任仍将落在治疗医师身上。

相似文献

2
Applying Artificial Intelligence to Gynecologic Oncology: A Review.应用人工智能于妇科肿瘤学:综述。
Obstet Gynecol Surv. 2021 May;76(5):292-301. doi: 10.1097/OGX.0000000000000902.

引用本文的文献

7
Machine learning in neuro-oncology: toward novel development fields.神经肿瘤学中的机器学习:迈向新的发展领域。
J Neurooncol. 2022 Sep;159(2):333-346. doi: 10.1007/s11060-022-04068-7. Epub 2022 Jun 28.

本文引用的文献

5
Survey on deep learning for radiotherapy.深度学习在放射治疗中的应用调查。
Comput Biol Med. 2018 Jul 1;98:126-146. doi: 10.1016/j.compbiomed.2018.05.018. Epub 2018 May 17.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验