Generative Artificial Intelligence in Prostate Cancer Imaging.

IF 1.9 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
Fahmida Haque, Benjamin D Simon, Kutsev B Özyörük, Stephanie A Harmon, Barış Türkbey
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引用次数: 0

Abstract

Prostate cancer (PCa) is the second most common cancer in men and has a significant health and social burden, necessitating advances in early detection, prognosis, and treatment strategies. Improvement in medical imaging has significantly impacted early PCa detection, characterization, and treatment planning. However, with an increasing number of patients with PCa and comparatively fewer PCa imaging experts, interpreting large numbers of imaging data is burdensome, time-consuming, and prone to variability among experts. With the revolutionary advances of artificial intelligence (AI) in medical imaging, image interpretation tasks are becoming easier and exhibit the potential to reduce the workload on physicians. Generative AI (GenAI) is a recently popular sub-domain of AI that creates new data instances, often to resemble patterns and characteristics of the real data. This new field of AI has shown significant potential for generating synthetic medical images with diverse and clinically relevant information. In this narrative review, we discuss the basic concepts of GenAI and cover the recent application of GenAI in the PCa imaging domain. This review will help the readers understand where the PCa research community stands in terms of various medical image applications like generating multi-modal synthetic images, image quality improvement, PCa detection, classification, and digital pathology image generation. We also address the current safety concerns, limitations, and challenges of GenAI for technical and clinical adaptation, as well as the limitations of current literature, potential solutions, and future directions with GenAI for the PCa community.

生成式人工智能在前列腺癌成像中的应用。
前列腺癌(PCa)是男性第二大常见癌症,具有重大的健康和社会负担,需要在早期发现,预后和治疗策略方面取得进展。医学影像学的进步显著影响了早期前列腺癌的检测、表征和治疗计划。然而,随着前列腺癌患者数量的增加和相对较少的前列腺癌成像专家,解释大量的成像数据是繁重的,耗时的,并且容易在专家之间发生变化。随着人工智能(AI)在医学成像领域的革命性进步,图像解释任务变得越来越容易,并有可能减少医生的工作量。生成式人工智能(GenAI)是最近流行的人工智能子领域,它创建新的数据实例,通常类似于真实数据的模式和特征。这一新的人工智能领域在生成具有多样化和临床相关信息的合成医学图像方面显示出巨大的潜力。在本文中,我们讨论了GenAI的基本概念,并介绍了GenAI在PCa成像领域的最新应用。这篇综述将帮助读者了解PCa研究界在各种医学图像应用方面的立场,如生成多模态合成图像、图像质量改进、PCa检测、分类和数字病理图像生成。我们还讨论了目前GenAI在技术和临床适应方面的安全性问题、局限性和挑战,以及当前文献的局限性、潜在的解决方案和GenAI在PCa社区的未来方向。
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来源期刊
Balkan Medical Journal
Balkan Medical Journal MEDICINE, GENERAL & INTERNAL-
CiteScore
4.10
自引率
6.70%
发文量
76
审稿时长
6-12 weeks
期刊介绍: The Balkan Medical Journal (Balkan Med J) is a peer-reviewed open-access international journal that publishes interesting clinical and experimental research conducted in all fields of medicine, interesting case reports and clinical images, invited reviews, editorials, letters, comments and letters to the Editor including reports on publication and research ethics. The journal is the official scientific publication of the Trakya University Faculty of Medicine, Edirne, Turkey and is printed six times a year, in January, March, May, July, September and November. The language of the journal is English. The journal is based on independent and unbiased double-blinded peer-reviewed principles. Only unpublished papers that are not under review for publication elsewhere can be submitted. Balkan Medical Journal does not accept multiple submission and duplicate submission even though the previous one was published in a different language. The authors are responsible for the scientific content of the material to be published. The Balkan Medical Journal reserves the right to request any research materials on which the paper is based. The Balkan Medical Journal encourages and enables academicians, researchers, specialists and primary care physicians of Balkan countries to publish their valuable research in all branches of medicine. The primary aim of the journal is to publish original articles with high scientific and ethical quality and serve as a good example of medical publications in the Balkans as well as in the World.
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