By Waqas Haque, MD, MPH This is the first post of our new blog series "The Way Ahead: Convergence of Technology and Cancer Care," in which we will delve into the latest technological advancements seeking to improve the care and outcomes of patients with cancer. In this edition, we explore the performance of a new artificial intelligence (AI) chatbot for providing National Comprehensive Cancer Network (NCCN) treatment recommendations, results of a multi-cancer early detection blood test stud...
Philipp Tschandl, MD, PhD, and colleagues found that current artificial intelligence (AI) algorithms that use "deep learning"—a type of machine learning that is based on artificial neural networks—outperform humans, even experts, in the classification of pigmented skin lesions. In this interview with i3 Health, Philipp Tschandl, member of the Vienna Dermatologic Imaging Research (ViDIR) Group of the Medical University of Vienna's Department of Dermatology, discusses the significance of the stud...
Cancer patients have to deal not only with symptoms of cancer and adverse effects due to treatments, but also with comorbidities such as depression, anxiety, and sleep disturbance. Because these symptoms greatly reduce patients' quality of life, it is imperative to combat them. Scientists have discovered how to better treat these symptoms by utilizing artificial intelligence (AI) in order to predict their presence and severity in cancer patients before they occur, allowing doctors to identify hi...
A new study reports that artificial intelligence (AI) in the form of machine-learning algorithms outperforms human experts in the diagnosis of pigmented skin lesions. This web-based study, which was published in The Lancet Oncology, included 511 human readers from 63 countries. Of these, 55.4% were board-certified dermatologists, 23.1% were dermatology residents, and 16.2% were general practitioners. The human readers were asked to diagnose dermatoscopic images that had been randomly selected in...
Due to lackluster recruiting techniques, a suboptimal number of patients are selected to participate in clinical trials. In addition, the researchers often have limited ability to observe and coach patients during clinical trials. These factors contribute to high clinical trial failure rates, which have a negative impact on the drug development cycle, not to mention 10 to 15 years and hundreds of millions of dollars wasted. However, scientists have proposed a potential solution to this problem: ...