Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice
This paper provides a consolidated list of challenges and solutions for researchers and developers aiming to deploy machine learning models in clinical radiology, serving as a practical guide for an incremental improvement in the field.
This paper identifies and discusses key challenges in developing and deploying machine learning models in clinical radiology, such as insufficient data, annotation costs, model generalization, and bias. It compiles a list of these considerations and outlines existing techniques to address them within the medical imaging context.
The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. Namely, we discuss: insufficient training data, decentralized datasets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen datasets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify techniques to address it. Although these techniques have been discussed in prior research literature, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders.