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Artificial Intelligence in Cancer Imaging
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Version 1
: Received: 3 January 2024 / Approved: 4 January 2024 / Online: 4 January 2024 (07:35:46 CET)
How to cite: Jonah, U.J.; Parwani, A. Artificial Intelligence in Cancer Imaging. Preprints 2024, 2024010335. https://doi.org/10.20944/preprints202401.0335.v1 Jonah, U.J.; Parwani, A. Artificial Intelligence in Cancer Imaging. Preprints 2024, 2024010335. https://doi.org/10.20944/preprints202401.0335.v1
Abstract
This manuscript examines the role of artificial intelligence in cancer imaging. It throws light on how artificial intelligence (AI) can significantly cut the wait time of patients and their clinicians in getting cancer diagnoses and assist health care providers by highlighting areas of an image where the interpreter needs to focus more for better result analysis and quality output. It also provides details on how the rise of AI attempts to standardize imaging results across providers by eliminating inter- and intra-observer variations among health care providers. Finally, it exposes the numerous limitations associated with AI use in cancer imaging, such as the need to digitize pathology laboratories and workflows, as well as transform our century-old tissue processing methods to fit into modern technological standards. In the field of radiology, the challenge of curating the enormous amount of data generated through MRIs, CT scans, PET scans, etc., poses a significant challenge to the ability of researchers to train relevant AI models to high levels of accuracy and reliability. In addition, over-reliance on AI by clinicians can deprive them of a common-sense approach to the health care issues of their patients and negatively impact doctor-patient relationships and confidentiality.
Keywords
Artificial Intelligence (AI); Cancer Imaging; Diagnostics
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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