Artificial Intelligence in medical imaging practice: looking to the future

Abstract

Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21st century. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these changes due to our large digital data footprint. Radiomics is transforming medical images into mineable high‐dimensional data to optimise clinical decision‐making; however, some would argue that AI could infiltrate workplaces with very few ethical checks and balances. In this commentary article, we describe how AI is beginning to change medical imaging services and the innovations that are on the horizon. We explore how AI and its various forms, including machine learning, will challenge the way medical imaging is delivered from workflow, image acquisition, image registration to interpretation. Diagnostic radiographers will need to learn to work alongside our ‘virtual colleagues’, and we argue that there are vital changes to entry and advanced curricula together with national professional capabilities to ensure machine‐learning tools are used in the safest and most effective manner for our patients.

Status of AI-enabled Clinical Decision Support Systems Clinical Implementations in China

“…Current systems were rarely embedded in multi-disciplinary or remote consultation systems and one of the most successfully executed functions of AI+CDSSs was imaging, followed by the previewing triage and risk factors screening. Our finding is consistent with a previous prediction that AI could foremost enter widespread use in medical imaging services (23,24). Chinese hospitals which have clinically tested or deployed AI+CDSSs tend to have higher grade, larger scale (actual opening beds, number of doctors and nurses) and bigger medical volume (quantity of outpatients, surgery, and discharged patients).…”

Radiological ‘SATs’ monitor: The use of ‘study ascribable times’ to assess the impact of clinical workload on resident training in a resource‐limited setting

“…Ongoing role‐extension and task‐shifting initiatives across the globe, which provide radiographer training in basic plain‐film reporting, represent attempts to mitigate the clinical impact of the reporting shortfall . Similarly, the exponential growth in artificial intelligence research in diagnostic imaging speaks to this evolving scenario . In the interim, clinicians rely largely on their own radiographic interpretive skills.…”

Artificial intelligence (AI) and big data in cancer and precision oncology

“…This is especially essential for detecting cancers early as it will ensure a better prognosis. AI has contributed to medical imaging by improving the quality of images, computer-aided image interpretation and radiomics, and the future of AI in medical imaging will focus on improving speed and cost reduction [38] , [39] .…”

“…There is a great need for medical imaging analysis using automated methods for standard clinical care. For accurate analysis of medical images, fulfilment of three strategies are required such as: i) image segmentation which identifies the image of interest and defines its boundaries, ii) image registration defines the spatial relationship between images, and iii) image visualisation extracts relevant data for accurate interpretation [38] , [40] . Despite the developments in medical imaging, there are challenges involved due to data complexity, object complexity and issues with validation.…”