Version 1
: Received: 24 May 2024 / Approved: 10 June 2024 / Online: 11 June 2024 (10:28:50 CEST)
How to cite:
Ozaki, Y.; Broughton, P.; Abdollahi, H.; Valafar, H.; Blenda, A. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review. Preprints2024, 2024060657. https://doi.org/10.20944/preprints202406.0657.v1
Ozaki, Y.; Broughton, P.; Abdollahi, H.; Valafar, H.; Blenda, A. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review. Preprints 2024, 2024060657. https://doi.org/10.20944/preprints202406.0657.v1
Ozaki, Y.; Broughton, P.; Abdollahi, H.; Valafar, H.; Blenda, A. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review. Preprints2024, 2024060657. https://doi.org/10.20944/preprints202406.0657.v1
APA Style
Ozaki, Y., Broughton, P., Abdollahi, H., Valafar, H., & Blenda, A. (2024). Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202406.0657.v1
Chicago/Turabian Style
Ozaki, Y., Homayoun Valafar and Anna Blenda. 2024 "Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202406.0657.v1
Abstract
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.
Keywords
omics technologies; artificial intelligence; cancer
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.
