A review in Nature Reviews Cancer emphasizes the necessity for cancer researchers to understand AI's potentials and limitations, detailing its applications and challenges in cancer research to aid non-computational biologists.
By Hugo Francisco de SouzaMay 20 2024Reviewed by Susha Cheriyedath, M.Sc. In a recent review published in the journal Nature Reviews Cancer, researchers present compelling arguments as to why a baseline understanding of the potentials and limitations of artificial intelligence applications is fast becoming necessary in today's war against cancer.
What is AI, and why should it matter in cancer research? Artificial intelligence is an umbrella term for many technologies and applications that attempt to simulate human intelligence and data processing using high-precision machine algorithms.
Cancer research is no different, with AI-based applications increasingly used in cellular and molecular image processing, histopathology research, and radiology. LLMs, in particular, are increasingly being used to collate and analyze clinical data, substantially improving the rate at which the data is processed and helping identify subtle patterns and trends within the data that would often be missed during manual human searches.
Understanding deep learning Herein, researchers introduce the theoretical framework governing classical machine learning algorithms and how these have evolved into the deep learning technologies of today. They differentiate between the different types of deep learning and their current applications in cancer research.
Artificial Intelligence Cell Deep Learning Drug Discovery Evolution Eye Histopathology Language Machine Learning Microscopy Research Software Technology
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