Artificial Intelligence in Kidney Cancer

Author:

Rasmussen Robert1,Sanford Thomas2,Parwani Anil V.3,Pedrosa Ivan145

Affiliation:

1. Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX

2. Department of Urology, Upstate Medical University, Syracuse, NY

3. Department of Pathology, The Ohio State University, Columbus, OH

4. Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX

5. Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX

Abstract

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes;Urology;2023-10

2. A Convolutional Neural Network of Low Complexity for Tumor Anomaly Detection;Proceedings of Eighth International Congress on Information and Communication Technology;2023-09-15

3. Kidney Cancer and all its Imaging Presentations, Implementation of Artificial Intelligence;2023 8th International Conference on Smart and Sustainable Technologies (SpliTech);2023-06-20

4. AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images;Bioengineering;2023-01-13

5. A Comparative Analysis of Different Models of Deep Learning for Prediction of Renal Cell Carcinoma;2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2022-12-23

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