A deep learning model successfully predicted the lung cancer survival period with an accuracy of 71.18%, outperforming previous machine learning models, according to the results of a study published in the International Journal of Medical Informatics. "Early detection and prediction of depth of survivability from cancer can help both patients and healthcare professionals better manage costs, treatment intensity and time spent around medical care," the authors wrote. The aim of this study was to characterize a deep learning approach to predict the survival period of patients with lung cancer. The study used data from the Surveillance, Epidemiology, and End Results (SEER) program. The deep learning models included data preprocessing using categorical and quantitative variables.

Machine learning increasingly supports physician decisions by making it easier to detect patterns in data as a means of predicting patient outcomes. In breast cancer, that now could apply to every stage of the experience, from diagnostics to mastectomy and breast reconstruction. At the annual meeting of the American Society of Clinical Oncology -- which was virtual this year, due to the ongoing coronavirus pandemic -- a consortium of researchers presented an abstract detailing how machine learning algorithms were able to correctly predict how individual patients would feel about their breast reconstruction.1 Using this tool in a clinical setting could help physicians guide patients through the recovery process in a way that better anticipates, and subsequently supports, their emotional reaction to this intensely personal medical procedure. Physician-researchers across 11 institutions in the United States and Canada trained 4 different types of machine learning algorithms -- regularized regression, Support Vector Machine, Neural Network, Regression Tree -- to predict with 95% accuracy whether a specific patient would be satisfied or dissatisfied with their breast reconstruction 2 years after their operation.

Artificial intelligence (AI) comprises a type of computer science that develops entities, such as software programs, that can intelligently perform tasks or make decisions.1 The development and use of AI in health care is not new; the first ideas that created the foundation of AI were documented in 1956, and automated clinical tools that were developed between the 1970s and 1990s are now in routine use. These tools, such as the automated interpretation of electrocardiograms, may seem simple, but are considered AI. Today, AI is being harnessed to help with "big" problems in medicine -- such as processing and interpreting large amounts of data in research and in clinical settings, including reading imaging or results from broad genetic-testing panels.1 In oncology, AI is not yet being used broadly, but its use is being studied in several areas.

Results of a study evaluating the use of artificial intelligence for matching oncology patients with clinical trials showed a 58.4% increase in trial enrollment compared with traditional screening methods. The findings from this study were presented at the IASLC 2019 World Conference on Lung Cancer hosted by the International Association for the Study of Lung Cancer in Barcelona, Spain. IBM Watson Health's Clinical Trials Matching (CTM) is a system that is trained to abstract patient- and clinical trial-specific data from a variety of sources and harnesses machine learning to find clinical trials that are appropriate for a given patient. In this study, CTM was trained and used to match patients at an academic oncology outpatient clinic to 1 or more of the approximately 42 available clinical trials in lung cancer. The matches generated by CTM were subsequently validated by clinical trial coordinators, and the validated matches were provided to oncologists prior to the patients' clinic visits.