… to accurately predict clinical outcomes that previously were difficult to model, including
time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model …

… Main Outcomes and Measures Clinical outcomes were … We trained a deep learning model
to automatically categorize … an association between clinical outcomes in psychotherapy and …

… deep learning models can be successfully trained on cohorts of only a few hundred patients
to accurately predict outcomes … Early successes in the application of deep learning to clinical …

… study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted
image data and evaluated the ability of these features in predicting clinical outcomes for …

… model for the clinical outcomes of patients after OHCA using deep-learning on data from
a … that the accurate performance of the deep-learning model, DCAPS, was excellent for …

… the clinical outcomes of cancer patients with using deep learning. In the feature learning …
the exportation of deep learning techniques for the purpose of learning more representative …

… and summarized deep learning performance in a number of clinical applications that …
clinical outcomes, and we compared the outcomes with those of conventional machine learning …

… outcomes when compared to the radiologists’ interpretation and to whole lung radiomics. In
conclusion, deep learning … and better prediction of clinical outcomes with respect to whole …

… when a patient receives a CT for clinical indications, this could have added value for
clinicians. In this study, we show how deep learning techniques learned from facial recognition …

… provide an inaccurate predictor of clinical outcome. Machine learning research in the form
of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel …