The University of Sydney revealed medical imaging will soon accommodate for the increasing use of Artificial Intelligence technologies. The published study covers current and potential new areas AI impacts work practices.
Artificial Intelligence Impact on Medical Imaging
Production of High-quality Images
Improvement of Image Interpretation
Improvement of Workflow
AI will not only introduce changes to the technical medical process but also to administration. Increased AI technology means potential cloud-based solutions for image and information management.
Such changes will require greater education on machine learning and NLP as future radiologists will need to have the skills to use offline and cloud-based tools for image processing, while also possessing the ability to recognise any potential errors produced by machine learning and the incorrect application of algorithms.
Production of High-quality Images
The involvement of AI and innovative visual technologies will naturally improve the production quality of medical imaging. Due to the changes to the contents of images, radiologists will need to apply deep learning-based tools for denoising medical images as well as generating routine-dose images low-dose ones while also perfecting image reconstruction.
Similar to the change in workflow, high-quality images will also require radiologists to use offline and cloud-based tools for image processing, denoising and reconstruction.
Improvement of Image Interpretation
The medical imaging industry has traditionally relied on human intelligence to produce results. However, many AI tools today demonstrate excellent progress in recognising patterns of disease of features such as the appearance of breast cancers on mammograms as well as analysing surrounding textual features. As such, AI will soon be able to provide a legitimate second diagnostic opinion on an image.
To become accustomed to this change, professionals in the medical imaging industry will need to understand the properties of machine learning tools to use them in the safest and most effective manner.
Image Segmentation
While image segmentation is already an efficient method of simplifying and partitioning digital imaging in medical imaging, the introduction of AI technologies will bring improvements to the system. Automating the segmentation of elements such as organs and lesions in the images will eliminate inter-observer variability and improve work efficiency.
To accurately make use of the automation, radiologists will need knowledge of machine learning to understand possible sources of errors in the segmentation conducted by machines.
Automating Image Registration
AI technology will ultimately automate the single and multimodality image fusion and registration.
This will require radiologists to understand offline and cloud-based image visualisation tools.
Radiomics Analysis
The process of analysing medical images will soon be further simplified by AI contributions. AI technology will transform medical images into mineable high-dimensional data to improve clincial decision making.
In order to adapt to this change, radiologists will need to gain familiarity with bioinformatics tools.
According to the authors, such changes to the medical imaging industry are bound to happen in the near future to accommodate for the integration of AI technologies. It is important to keep in mind such future possibilities to maximise learning opportunities and prepare for industry changes.
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