Luke Oakden-Rayner

TL;DR: In this article, the authors argue that explainability is a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support, and advocate for rigorous internal and external validation of AI models as a more direct means of achieving the goals often associated with explainability.

TL;DR: Evidence is found that hidden stratification can occur in unidentified imaging subsets with low prevalence, low label quality, subtle distinguishing features, or spurious correlates, and that it can result in relative performance differences of over 20% on clinically important subsets.

TL;DR: In this paper, a single model that directly combines image features, patient and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital processes data.

TL;DR: Visual inspection of images is a necessary component of understanding large image datasets and teams producing public datasets should perform this important quality control procedure and include a thorough description of their findings, along with an explanation of the data generating procedures and labeling rules, in the documentation for their datasets.

TL;DR: In this article, the authors demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques.