EventsMOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published
with-doi10.3390/mol2net-09-14283 (registering DOI)
This submission belongs to the session 05. USEDAT.NET: USA-Europe Data Analysis Trends & Complex Networks Mini Congress Series, Coruña, SP-Miami, USA, 2023 of the event MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published date
24 Mar, 2023
Academic Editor
author-avatarMOL2NET Team
Citation
Shumin Ren, The recent development of artificial intelligence-based cancer occurrence risk prediction models, in Proceedings of MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed., 25 December–31 December 2023, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-09-14283
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The recent development of artificial intelligence-based cancer occurrence risk prediction models

1. Translation Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China, China
2. Department of Computer Science and Information Technology, University of A Coruña, 15071, A Coruña, Spain
Abstract

Artificial intelligence (AI) is playing an increasingly important role in developing cancer occurrence risk models. AI model can analyze vast amounts of data to identify patterns and correlations that may not be immediately apparent to clinicians, which can reduce overdiagnosis, timely identify risk factors, and lower incidence and mortality rates. This mini-review presented three specific articles that demonstrate the development process and application effectiveness of AI-based cancer occurrence risk models, providing inspiration and reference for future developments. These research allows for more accurate predictions of cancer risk based on a variety of factors such as imaging results, blood test result, etc. By identifying individuals at high risk for developing cancer, preventative measures can be taken to reduce their likelihood of developing the disease. Additionally, AI can help reduce overdiagnosis by distinguishing between benign and malignant conditions with greater accuracy. Overall, the use of AI in developing cancer risk models has the potential to greatly improve our ability to prevent and treat cancers.

Keywords
Artificial intelligence
machine learning
cancer risk prediction model
cancer prevention
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