Artificial intelligence-based biomarkers for treatment decisions in oncology
- PMID: 39814650
- DOI: 10.1016/j.trecan.2024.12.001
Artificial intelligence-based biomarkers for treatment decisions in oncology
Abstract
The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential treatment options could increase by tenfold over the next 5 years. The cost of personalized medicine technologies limits its accessibility, thus increasing socioeconomic disparities in the treated population. In this review we describe artificial intelligence (AI)-based solutions - including deep learning (DL) methods for routine medical imaging and large language models (LLMs) for electronic health records (EHRs) - to support cancer treatment decisions with cost-effective biomarkers. We address the current limitations of these technologies and propose the next steps towards their adoption in routine clinical practice.
Keywords: artificial intelligence; biomarkers; medical imaging; oncology; personalized medicine.
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
Declaration of interests J.N.K. declares consulting services for Owkin (France), DoMore Diagnostics (Norway), Panakeia (UK), AstraZeneca (UK), Scailyte (Switzerland), Mindpeak (Germany), and MultiplexDx (Slovakia). He also holds shares in StratifAI GmbH (Germany) and Synagen GmbH (Germany), is lead investigator on a research project funded by GSK, and has received honoraria by AstraZeneca, Bayer, Eisai, Janssen, MSD, BMS, Roche, Pfizer, and Fresenius. O.S.M.E.N. holds shares in StratifAI GmbH. M.A. declares research funding from Amgen, AstraZeneca, and Sandoz, and advisory services for Viatris. M.L. declares no competing interests.
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