Anonymisation of data should be guaranteed; this involves more than de-identification and should ensure no capability to re-identify the patient through DICOM tags, facial recognition software, etc. Furthermore, if patient data are used to build AI products which go on to generate profit, consideration needs to be given to the issue of intellectual property rights.
Do the involved patients and the collecting organisations have a right to share in the profits that derive from their data? One of the many exciting prospects for AI incorporation into radiology practice is the potential to extract data from large numbers of studies, unconstrained by location, and use that data to identify imaging markers that may predict outcome or response to treatment — Radiomics. Neural network analysis of large datasets can isolate important relationships that could never be perceived by visual interpretation of studies alone, and which may prove important in future personalised healthcare.
However, given that these relationships are the products of mathematical algorithms, they may not always make sense or be relevant. The Texas sharpshooter fallacy is a term applied to the reasoning that may apply: a sharpshooter covers a wall with targets, fires his gun, and displays whatever target he hits or draws a target around his bullet hole, having fired at a blank wall to display his prowess, ignoring all the targets missed.
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This is not intended to suggest that all data mining or radiomics falls into this fallacy; far from it. However, some data mining outputs will be irrelevant or irrational. The ethical issues arising from AI use in radiology will be considered in greater detail in a multi-society including the ESR paper currently in preparation and will not be explored in depth here. Some of the points considered above encompass ethical elements. Fundamentally, it must be remembered that AI tools are mathematical constructs, designed to optimise a mathematical function.
Intrinsically, they are amoral. The challenge for humans is to anticipate how AI systems can go wrong or could be abused and to build in protections against this happening [ 68 ]. Interpretation skills strongly depend on the number of exams interpreted and the accuracy of the visual image analysis. AI can perform image reading by exploiting deep learning tools and is able not only to extract visual information but also quantitative information, such as Radiomic signatures or other imaging biomarkers, which would not be identified by the human brain. AI is going to become part of our image viewing and analysis toolset.
The other face of this coin is that trainees will be helped by AI to perform better interpretations; nonetheless, a strong dependence of future radiologists on aid from AI software is a risk, with potentially deleterious consequences. The implementation of AI in radiology requires that trainees learn how to best integrate AI in radiological practice, and therefore a specific AI and informatics module should be included in the future radiology training curricula.
AI involvement in our professional lives is inevitable. A vast amount of AI research is ongoing; image interpretation is an attractive target for researchers, given that the tasks involved at least in part involve analysis of large amounts of data to produce an output. Radiologists cannot, and should not, wish this research away, but rather embrace it and integrate it as much as possible into the daily work, guiding AI research directions to ensure the maximum clinical benefit to patients from new developments.
Another task that must be taken on is leadership in educating policymakers and payers about radiology, AI, their integration, and the associated pitfalls. In any rapidly developing industry, there is initial excitement, often followed by disappointment when early promises are unfulfilled [ 69 ]. The hype around new technology, often commercially-driven, may promise more than it can deliver, and tends to underplay difficulties. It is the responsibility of clinical radiologists to educate themselves about AI in radiology, and in turn to educate those who manage and fund our hospitals and healthcare systems, to maintain an appropriate and safe balance protecting patients while implementing the best of new developments.
The simple answer is: NO. Many of the single routine tasks in the radiology workflow will be performed faster and better by AI algorithms, but the role of the radiologist is a complex one, focused on solving complex clinical problems [ 68 ]. The real challenge is not to oppose the incorporation of AI into the professional lives a futile effort but to embrace the inevitable change of radiological practice, incorporating AI in the radiological workflow [ 12 ]. Radiologists can avoid this by educating themselves and future colleagues about AI, collaborating with researchers to ensure it is deployed in a useful, safe, and meaningful way, and ensuring that its use is always directed primarily towards the patient benefit.
In this way, AI can enhance radiology, and allow radiologists to continually improve their relevance and value [ 70 , 71 ]. Morgenstern M Automation and anxiety. Accessed 7 Aug Mukherjee S A. I Versus M. New Yorker. Morgan Kaufmann Publishers, Inc. Turing A On computable numbers, with an application to the Entscheidungsproblem. Proc Lond Math Soc 2nd Ser — Oxford University Press. Tech rep, Dartmouth College.
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The ultimate guide to AI in radiology
Eur Radiol Exp 2 1 Download references. This paper was prepared by Prof. The authors gratefully acknowledge the valuable contribution to the paper of Dr. Angel Alberich Bayarri, Prof. Christoph D. Becker, Dr. Francesca Coppola, and Dr. EN: conception and design of the paper, manuscript drafting and review. NdS: manuscript drafting and review. AB: manuscript drafting and review. All contributors read and approved the final manuscript. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Reprints and Permissions. Search all SpringerOpen articles Search. Abstract This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Key points Outside the traditional radiology activities of image interpretation, AI is estimated to impact on radiomics, imaging biobanks, clinical decision support systems, structured reporting, and workflow.
Introduction Artificial Intelligence AI is one of the fastest-growing areas of informatics and computing with great relevance to radiology. Using contrast or imaging agents that attach to specific molecules, disease processes, such as cancer, can be spotted before they render their effects at the level of gross pathology. Optical coherence tomography OCT is a newer form of CT being used in research that constructs images from light that is transmitted and scattered through the body. The power of ultrasound is being used in conjunction with microbubbles.
The microbubbles can be injected directly into a specific location and then burst via ultrasound to emit localized contrast agents for imaging, chemotherapy for cancer treatment, air to help dissolve clots, and genes or drugs which can more easily penetrate cell membranes that are weakened by ultrasound. New imaging techniques bring new means for peering into the human body, helping to reduce the need for more invasive diagnostic and treatment procedures. Biomedical image processing is similar in concept to biomedical signal processing in multiple dimensions.
It includes the analysis, enhancement and display of images captured via x-ray, ultrasound, MRI, nuclear medicine and optical imaging technologies. Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. When the original CT scanner was invented in , it literally took hours to acquire one slice of image data and more than 24 hours to reconstruct that data into a single image.
Today, this acquisition and reconstruction occurs in less than a second. Rather than simply eyeball an x-ray on a lightbox, image processing software helps to automatically identify and analyze what might not be apparent to the human eye. Computerized algorithms can provide temporal and spatial analysis to detect patterns and characteristics indicative of tumors and other ailments.
Depending on the imaging technique and what diagnosis is being considered, image processing and analysis can be used to determine the diameter, volume and vasculature of a tumor or organ; flow parameters of blood or other fluids and microscopic changes that have yet to raise any otherwise discernible flags.
Deep Learning Applications in Medical Imaging
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