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 (AI) applications is fast becoming necessary in today's war against cancer. They briefly introduce AI and its associated models (artificial neural networks (ANNs), deep learning, and large language models [LLM]), and highlight advances in the field and their application in cancer research, and the challenges faced in ubiquitous AI technology adoption in ongoing studies. This review is meant to serve as a practical guideline for AI's adoption into mainstream cancer research, primarily targeted at non-computationally inclined cancer biologists.
It provides numerous examples of how the technology can hasten research progress and identify patterns invisible to the naked human eye. Review Article: A guide to artificial intelligence for cancer researchers . Image Credit: springsky / Shutterstock Artificial intelligence (AI) is an umbrella term for many technologies and applications that attempt to simulate human intelligence and data processing using high-precision machine algorithms.
Despite being widely regarded as originating during a conference in 1956 (Dartmouth College), AI remained a theoretical rule-based system for most of its existence, with the now-called 'symbolic AI' and 'classical machine learning' dominating the field until as recently as the past 15 years. Unpreced.