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The work highlights needs for higher prediction accuracy and for clinically feasible adaptive combination therapy models that reflect newly approved therapeutics and expanding multimodal cancer data. Using nonlinear system control theory, the review integrates GenAI learning for treatment-response prediction and recommendations, combining nonlinear system identification, control theory, and deep learning within an adaptive cancer control framework.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/generative-ai-assisted-adaptive-cancer-therapy-article-review/365884/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/generative-ai-assisted-adaptive-cancer-therapy-article-review/365884.png","ImageObject",300,407,{"name":92,"@type":93},"Sarah ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of adaptive combination therapy in cancer treatment?","Question",{"text":112,"@type":113},"Adaptive combination therapy aims to prevent or delay therapeutic resistance by dynamically tuning treatment plans according to evolving cancer dynamics and monitoring treatment responses.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the review connect GenAI with adaptive cancer therapy?",{"text":117,"@type":113},"It proposes using GenAI learning and inference to predict treatment response changes and recommend treatments within an adaptive therapy framework, integrating nonlinear system identification, control theory, and deep learning.",{"name":119,"@type":110,"acceptedAnswer":120},"What challenges must be addressed for GenAI-assisted oncology in real-world workflows?",{"text":121,"@type":113},"The document emphasizes that access to clinical data, deep learning opacity, and clinical validation are critical challenges requiring careful attention to support adoption and integration in practice.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},365884,1790616610,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962085320529,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Generative AI-Assisted Adaptive Cancer Therapy  \nCancer Control Volume 32: 1-17 © The Author(s) 2025 Article reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/10732748251349919](DOI: 10.1177/10732748251349919)  \n[journals.sagepub.com/home/ccx](journals.sagepub.com/home/ccx)  \nYoucef Derbal, PhD 1 􀀁  \nAbstract  \nAdaptive combination therapy is deemed the most intuitive strategy to thwart therapeutic resistance through dynamic treatment tuning that accounts for cancer evolutionary dynamics. However, higher accuracy and reliability of treatment response predictions would be needed, in addition to the need for clinically feasible models of adaptive combination therapy that consider newly approved therapeutics and the growing multimodal data being available about cancer. Grounded in nonlinear system control theory, this review offers a perspective on exploiting GenAI learning and inferencing capabilities to predict treatment response and recommend treatments in the context of adaptive cancer therapy. Results from nonlinear system identification, control theory and deep learning are integrated within an adaptive cancer control framework to leverage the continuously expanding data about cancer and its treatment towards GenAI-enhanced adaptive therapy. The resulting models and their analysis contribute to a much-needed conceptual clarity about the research and translational pathways that would be needed to realize GenAI-assisted cancer treatments. In particular, they underscore that access to clinical data, deep learning opacity, and clinical validation present critical challenges that require adequate attention to pave the way towards acceptance and integration of GenAI in real-world oncology workflows.  \nPlain Language Summary  \nDespite the increasing number of treatment options being available to cancer patients, most of these treatments stop working after some time due to the cancer becoming resistant. One of the strategies to thwart cancer resistance is to design treatments that are one-step-ahead of cancer by using drug combinations and adapting therapies based on treatment response monitoring. This treatment approach is called adaptive therapy. This study explores the use of generative artificial intelligence (GenAI) models to enhance adaptive therapy by predicting changes of the disease and accordingly adapting treatments based on learning acquired by these models through training on large amount of data about cancer and patient treatments. The study highlights a pathway for the clinical evaluation of this GenAI-assisted approach to cancer therapy and discusses the challenges associated with its implementation and deployment in clinics.  \nKeywords  \ngenerative artificial intelligence, cancer treatment, clinical decision support systems, treatment response monitoring, adaptive treatment scheduling, rapid learning healthcare systems, adaptive cancer therapy, large language models, adaptive treatment planning  \nReceived: January 30, 2025; revised: May 22, 2025; accepted: May 28, 2025.  \nIntroduction  \nCancer is complex adaptive system 1-4 with nonlinear timevarying dynamics shaped by genetic5-7 immunological8-12 and eco-evolutionary determinants.13-16 These causal dimensions of cancer complexity are bound to genetic diversity and phenotypic plasticity which support a near-boundless capacity  \n1 Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada  \nCorresponding Author:  \nYoucef Derbal, Ted Rogers School of Information Technology Management, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.  \nEmail: [yderbal@torontomu.ca](yderbal@torontomu.ca)  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons  \nAttribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creative","cbCaikthwx6CQfI0","https://ap.wps.com/l/cbCaikthwx6CQfI0","pdf",1061044,17,"English","# Abstract\n# Plain Language Summary\n# Keywords\n# Introduction","[{\"question\":\"What is the main goal of adaptive combination therapy in cancer treatment?\",\"answer\":\"Adaptive combination therapy aims to prevent or delay therapeutic resistance by dynamically tuning treatment plans according to evolving cancer dynamics and monitoring treatment responses.\"},{\"question\":\"How does the review connect GenAI with adaptive cancer therapy?\",\"answer\":\"It proposes using GenAI learning and inference to predict treatment response changes and recommend treatments within an adaptive therapy framework, integrating nonlinear system identification, control theory, and deep learning.\"},{\"question\":\"What challenges must be addressed for GenAI-assisted oncology in real-world workflows?\",\"answer\":\"The document emphasizes that access to clinical data, deep learning opacity, and clinical validation are critical challenges requiring careful attention to support adoption and integration in practice.\"}]","Generative AI-Assisted Adaptive Cancer Therapy - Article review | PDF",1790162807,43]