[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124700-en":3,"doc-seo-124700-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124700,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","The Evolution and Reliability of Machine Learning Techniques for Oncology","Internet and other digital innovations have reignited interest in AI, particularly machine learning (ML) methods that aim to extract patterns from high-dimensional data. Despite regular use in medical science, applying ML to improve patient care has progressed slowly due to hurdles such as the availability of diverse curated datasets, costs and time for data collection and model development, and integration trade-offs. This article evaluates the validity of ML in cancer and provides a framework for oncology research that can generalize to related disciplines.","Paper—The Evolution and Reliability of Machine Learning Techniques for Oncology  \nThe Evolution and Reliability of Machine Learning Techniques for Oncology  \n[https://doi.org/10.3991/ijoe.v19i08.39433](https://doi.org/10.3991/ijoe.v19i08.39433)  \nHamza Abu Owida1, BasharAl-haj Moh’d1, Nidal Turab2(*), Jamal Al-Nabulsi1,  \nSuhaila Abuowaida3  \n1Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University,  \nAmman, Jordan  \n2Department of Networks and Cyber Security, Faculty of Information Technology, Al-Ahliyya  \nAmman University, Amman, Jordan  \n3Department of Computer Science, Prince Hussein Bin Abdullah Faculty of Information  \nTechnology, Alal-Bayt University, Mafraq, Jordan  \n[n.turab@ammanu.edu.jo](n.turab@ammanu.edu.jo)  \nAbstract—It is no secret that the rise of the Internet and other digital technologies has sparked renewed interest in AI-based techniques, especially those that fall under the umbrella of the subset of algorithms known as “Machine Learning”(ML) . Electronic innovations have enabled us to comprehend the universe beyond the limits of human cognition. The difficult nature of a high-dimensional dataset. Although these techniques have been regularly employed by the medical sciences, their adoption to enhance patient care has been a bit slow. The availability of curated diverse data sets for model development is all examples of the substantial hurdles that have delayed these efforts. The future clinical acceptance of each of these characteristics may be affected by a number of limiting conditions, such as the time and resources spent on data collection and model development, the cost of integration relative to the time and resources spent on translation, and the potential for patient damage. In order to preserve value and enhance medical care, the goal of this article is to evaluate all facets of the issue in light of the validity of using ML methods in cancer, to serve as a template for further research and the subfield of oncology that serves as a model for other parts of the discipline.  \nKeywords—Machine Learning, oncology, cancer classification  \n1 Introduction  \nMachine learning (ML) methods and their accompanying use cases have been steadily expanding over the past two decades. There are many examples of ML’s subtle but pervasive presence in our daily lives, from shopping suggestion software to advanced image and speech recognition systems. The presence of ML techniques is also felt in the workplace by scientists and doctors, in the form of a plethora of algorithms and ML-based tools that assist and, in some cases, have come to replace human practice in the biomedical sciences [1, 2] .  \nPaper—The Evolution and Reliability of Machine Learning Techniques for Oncology  \nThe term “machine learning”(ML) is used to describe a wide range of computational methods that help computers “learn,” or improve their proficiency at a given activity, over time. The ML method relies on a large amount of data that the computer system iteratively examines in an effort to minimize the difference between its forecast and the expected result [3, 4] .  \nThe breakthroughs in computing and digital technology that made machine learning approaches possible also greatly increased data-acquisition and data-storage capabilities in a variety of scientific study disciplines, thereby ushering in the so-called ‘Big Data’movement. In turn, the enormous quantity of data made it possible for ML methods to be effectively applied in various fields. ML is becoming increasingly prevalent in the medical practice, and oncology is no exception. This is true across the board, from approaches that aid in diagnosis by uncovering complicated patterns in screening data to expert systems that decide treatment recommendations [5, 6] . Figure 1 shows the block diagram of Machine Learning algorithm.  \nFig. 1. The block diagram explains the working of Machine Learning algorithm  \nSince a few decades ago, the fields of medical radiolo","cbCaio1fsiXQuB8d","https://ap.wps.com/l/cbCaio1fsiXQuB8d","pdf",1129322,1,20,"English","en",105,"# Introduction\n# ML fundamentals and data requirements\n# Big Data and oncology applications\n# Deep learning advances and CNN-driven perception\n# Reliability challenges and AI winters","[{\"question\":\"Why has adopting machine learning in oncology been slower than expected?\",\"answer\":\"Key barriers include limited availability of diverse curated datasets and constraints related to time, resources, and integration costs for data collection, model development, and deployment.\"},{\"question\":\"How does the article describe the role of big data in enabling ML?\",\"answer\":\"Advances in computing and digital infrastructure increased data acquisition and storage, supporting the application of ML across scientific fields, including medical practice and oncology.\"},{\"question\":\"What reliability challenges does the paper associate with ML and deep learning systems?\",\"answer\":\"The paper highlights difficulties such as complex implementation, the need for additional contextual information, and concerns about opaque “black-box” behavior, which have contributed to periods like “AI winters.”\"}]","The Evolution and Reliability of Machine Learning Techniques for Oncology | 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has adopting machine learning in oncology been slower than expected?","Question",{"text":75,"@type":76},"Key barriers include limited availability of diverse curated datasets and constraints related to time, resources, and integration costs for data collection, model development, and deployment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the article describe the role of big data in enabling ML?",{"text":80,"@type":76},"Advances in computing and digital infrastructure increased data acquisition and storage, supporting the application of ML across scientific fields, including medical practice and oncology.",{"name":82,"@type":73,"acceptedAnswer":83},"What reliability challenges does the paper associate with ML and deep learning systems?",{"text":84,"@type":76},"The paper highlights difficulties such as complex implementation, the need for additional contextual information, and concerns about opaque “black-box” behavior, which have contributed to periods like “AI 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