{"title":"Toward a reduction in the burden of therapy in patients with rhabdomyosarcoma: How much is enough?","authors":"Ewa Koscielniak MD, Thomas Klingebiel MD","doi":"10.1002/cncr.35743","DOIUrl":"https://doi.org/10.1002/cncr.35743","url":null,"abstract":"","PeriodicalId":138,"journal":{"name":"Cancer","volume":"131 4","pages":""},"PeriodicalIF":6.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>A new ChatGPT-like artificial intelligence (AI) model developed by researchers at Harvard Medical School outperforms other state-of-the-art AI methods by up to 36% in an array of diagnostic tasks across multiple forms of cancer. These tasks include the detection of cancer cells, the identification of a tumor’s origin, the prediction of patient outcomes, and the identification of the presence of genes and DNA patterns associated with treatment response.<span><sup>1</sup></span><sup>,</sup>