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Artificial intelligence methods available for cancer research
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Artificial intelligence methods available for cancer research
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.
machine learningartificial neural networkdeep learningnatural language processingpredictionguidelinediagnosis
Ankita Murmu、Balázs Gy?rffy
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Institute of Molecular Life Sciences,HUN-REN Research Centre for Natural Sciences,Budapest 1117,Hungary
National Laboratory for Drug Research and Development,Budapest 1117,Hungary
Department of Bioinformatics,Semmelweis University,Budapest 1094,Hungary
Department of Biophysics,University of Pecs,Pecs 7624,Hungary
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machine learning artificial neural network deep learning natural language processing prediction guideline diagnosis
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