Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have numerous promising applications in several fields of medicine. Its use has grown following the increased availability of patient data due to technological advances such as digital health records and high-volume information extraction from medical images. Multiple ML algorithms have been proposed for applications in oncology. For instance, they have been employed for oncological risk assessment, automated segmentation, lesion detection, characterization, grading and staging, prediction of prognosis and therapy response. In the near future, ML could become essential part of every step of oncological screening strategies and patients' management thus leading to precision medicine.

Machine Learning in Oncology: A Clinical Appraisal / Cuocolo, Renato; Caruso, Martina; Perillo, Teresa; Ugga, Lorenzo; Petretta, Mario. - In: CANCER LETTERS. - ISSN 0304-3835. - (2020). [10.1016/j.canlet.2020.03.032]

Machine Learning in Oncology: A Clinical Appraisal

Cuocolo, Renato
Writing – Review & Editing
;
Caruso, Martina
Writing – Original Draft Preparation
;
Perillo, Teresa
Data Curation
;
Ugga, Lorenzo
Formal Analysis
;
Petretta, Mario
Supervision
2020

Abstract

Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have numerous promising applications in several fields of medicine. Its use has grown following the increased availability of patient data due to technological advances such as digital health records and high-volume information extraction from medical images. Multiple ML algorithms have been proposed for applications in oncology. For instance, they have been employed for oncological risk assessment, automated segmentation, lesion detection, characterization, grading and staging, prediction of prognosis and therapy response. In the near future, ML could become essential part of every step of oncological screening strategies and patients' management thus leading to precision medicine.
2020
Machine Learning in Oncology: A Clinical Appraisal / Cuocolo, Renato; Caruso, Martina; Perillo, Teresa; Ugga, Lorenzo; Petretta, Mario. - In: CANCER LETTERS. - ISSN 0304-3835. - (2020). [10.1016/j.canlet.2020.03.032]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/801399
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