Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.Author summary: Machine learning is becoming an important part of digital health and medical imaging. Many believe that it is the solution to some of the challenges our medical systems currently face. In this study, we reviewed the literature to explore this topic, focusing on the promises, challenges, and future developments. The literature emphasises that machine learning allows us to use medical images in ways that are more reliable and precise, requiring less time and resources. That can lead to better decision-making, as well as allow for more people to access affordable image-based care. Some of the mentioned challenges include the large differences between images and imaging techniques, the difficulty in accessing enough high-quality images, the costs and infrastructure associated with that and the resulting geographic inequalities. In addition, the literature emphasizes that is difficult to understand how machine learning works, as well to assess how valid and reliable it is in analysing medical images. Equally difficult is its regulation and integration in everyday clinical work. The future is expected to bring machine learning models that will be able to analyse different types of images and other clinical data at once, in ways that are more transparent and understandable.