… diagnosis, prognosis, treatment response, or genomic status (Table 2). In this review, we
summarize DL applications in cancer … involved in virtually every cancer diagnosis, and consider …

… cancer is, therefore, vital to its effective and individualized clinical care. The support vector
machine (SVM) is a rising machine … both diagnosis and treatment of most aggressive cancers, …

… Cancer is a complex and multifaced disorder with thousands of genetic … diagnosis, prognosis,
and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis …

… The structure we most often refer to when talking about laboratory diagnostic pathways
would be … on histologic slides have proven to be useful for the detection of cancer cells [28,29]. …

… different types of genomic markers in cancer. This is a discovery type … Machine learning
applications to identify clinical populations at risk for negative outcomes and to aid with diagnosis …

… screening programmes such as breast cancer, there are ongoing debates … potential to directly
facilitate cancer diagnosis by triggering … of AI for early cancer diagnosis in symptomatic and …

… used in clinical oncology to diagnose cancers, predict patient … and solid tumor biopsies
for cancer diagnosis, prognosis, and … Finally, we examine ML models approved for cancer-related …

… We identify mitigation strategies to address ongoing challenges around data availability
and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into …

… Ultimately she was diagnosed with breast cancer in 2014, but the path to that diagnosis
had been unbelievably frustrating. “How do you do three tests and get three different results?” …

… data can be used to predict mutations in lung cancers and the response to chemotherapy.Many …
help clarify or make the diagnosis, potentially sparing the patient additional diagnostic …