Artificial intelligence methods available for cancer research

Abstract

Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles—a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.

Keywords

machine learning / artificial neural network / deep learning / natural language processing / prediction / guideline / diagnosis

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Acknowledgements

This work was supported by the National Research, Development, and Innovation Office (PharmaLab, RRF-2.3.1-21-2022-00015 and TKP2021-NVA-15). The manuscript has been edited using a GPT platform to improve grammar. Ankita Murmu and Balázs Győrffy acknowledge the support of ELIXIR Hungary. Ankita Murmu is grateful to Tempus Public Foundation (Hungary) for the Stipendium Hungaricum Ph.D. Scholarship.

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Conflict of interest Ankita Murmu and Balázs Győrffy declares no potential conflict of interest.

This manuscript is a review article and does not involve a research protocol requiring approval by the relevant institutional review board or ethics committee.

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