A framework for artificial intelligence in cancer research and precision oncology
Raquel Pérez-López, Jorge S. Reis‐Filho, Jakob Nikolas Kather
Abstract
This collection aims to disseminate the latest research and advancements in all aspects of artificial intelligence (AI) within the realm of cancer research, including basic, translational, and clinical research. The collection is undoubtedly ambitious, as it seeks to present a comprehensive review of current AI applications in precision oncology while offering expert insights on accelerating the transition of AI tools from the laboratory to the clinic, with the aim of ultimately enhancing patient care and improving clinical endpoints. Priority will be given to articles that employ innovative methodologies, address pertinent real-world issues, and provide robust evidence through the utilization of multicentric datasets. Although the definition of AI remains ambiguous, most contemporary, successful AI systems incorporate some form of deep learning 1 . Within the biomedical research literature, there is a prevailing consensus that the term “AI” can be applied to deep learning approaches—those involving models with millions or billions of parameters—rather than simple statistical models such as linear regression with only a few free parameters and co-variables 1 . Although deep learning approaches are the primary focus of this collection, we remain receptive to related fields.