Deep Learning for Clinical Imaging Diagnosis at Scale
Keywords:
Medical imaging; deep learning; imaging diagnoses; convolutional neural networks; transformer; multicenter clinical datasets ,Data Generation Metrics, Biases, and Capability, Generative AI Model Evaluation in Healthcare.Abstract
Recent advances in deep learning have generated excitement within the domain of medical imaging diagnostics. Promising results achieved in specific tasks, often with limited training data, have led to calls for scaled-up applications leveraging large-scale medical imaging datasets. Such an approach could facilitate rapid and cost-effective development of diagnostic algorithms based on different imaging modalities. Nevertheless, a crucial step toward subsequent clinical uptake of these methods is the successful deployment of a large, multicenter test set for performance evaluation. However, despite being essential to the transfer of deep learning methods into clinical practice, scalable evaluation and deployment of these technologies have yet to receive significant attention. The attractions of large-scale testing and the potential effects of scale on accuracy are therefore often overlooked.
Analysis of deep learning methods — including convolutional neural networks, transformers, and other architectures — capable of utilizing large-scale clinical imaging datasets to achieve comparable or superior sensitivity, specificity, and area under the curve values to human clinicians is presented. Key strengths of the methodology include the emphasis on large-scale datasets, consideration of diagnostic performance across diverse clinical specialties, and application of external validation datasets to assess generalization performance across multiple sites. These qualities make the method particularly suitable for the evaluation of potentially biased or miscalibrated systems.
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