{"title":"Introducción al machine learning en Senología","authors":"Eduardo Alcobilla Ferrara","doi":"10.1016/j.senol.2023.100503","DOIUrl":null,"url":null,"abstract":"<div><p>Machine Learning or Statistical Learning is a concept belonging to the field of Computer Science that refers to the ability of machines to build mathematical models with high predictive efficiency from large data packages through a series of tools based on Statistics, Algorithmics and recursion, and to improve them as new information is incorporated.</p><p>Although in the field of breast disorders there are already various projects mainly oriented to the interpretation of images in both Radiology and Pathology, the systematic use of this technology as a source of acquiring new knowledge is still exceptional, particularly with regard to clinical-therapeutic decision-making. All the specialists involved in the field of breast disorders are obliged to familiarize ourselves with this methodology, in order to properly direct it away from its use for profit, especially when the successes achieved in other social spheres allow us to intuit that its implementation in Medicine can not only be useful but unavoidable.</p></div>","PeriodicalId":38058,"journal":{"name":"Revista de Senologia y Patologia Mamaria","volume":"36 4","pages":"Article 100503"},"PeriodicalIF":0.3000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista de Senologia y Patologia Mamaria","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0214158223000336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Machine Learning or Statistical Learning is a concept belonging to the field of Computer Science that refers to the ability of machines to build mathematical models with high predictive efficiency from large data packages through a series of tools based on Statistics, Algorithmics and recursion, and to improve them as new information is incorporated.
Although in the field of breast disorders there are already various projects mainly oriented to the interpretation of images in both Radiology and Pathology, the systematic use of this technology as a source of acquiring new knowledge is still exceptional, particularly with regard to clinical-therapeutic decision-making. All the specialists involved in the field of breast disorders are obliged to familiarize ourselves with this methodology, in order to properly direct it away from its use for profit, especially when the successes achieved in other social spheres allow us to intuit that its implementation in Medicine can not only be useful but unavoidable.