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What the radiologist should know about artificial intelligence – an ESR white paper

Did you ever get lost between all the buzz words floating around like artificial intelligence, imaging informatics, radiomics, imaging biobanks, neural networks, clinical decision support, etc.?

Just as Amazon’s recruitment tool showed bias against women and was finally shut down, if not implemented properly, an artificial intelligence tool could tend to suggest not treating patients of a specific subgroup because of an unidentified bias in the training data.

Rather, we should educate ourselves and be aware of areas where these algorithms could really assist with tedious tasks and areas where more caution and validation is needed to ensure we continue to provide high-quality service and value to our patients.

VIDEO: How Utilization of Artificial Intelligence Will Impact Radiology

Here is an example of new artificial intelligence (AI) driven health information systems that aim to improve efficiency, speed workflow and make finding relevant patient data fast.

All are clickable and show a small pop-up box with a key image and brief description, and a link to the full set of images and the full report.  Other boxes on the screen help organize the relevant patient data into easily digestible bits with links to full reports or areas of the patient record.

Artificial intelligence: a challenge for third millennium radiologist

They produce a large number of images, on which the radiologist writes, after a subjective and personal evaluation, essentially qualitative, the diagnosis, the so-called report.

The ambitious objective is to identify, thanks to complex mathematical and statistical algorithms, which information, inaccessible to simple visual analysis, can predict the evolution of the disease and the effectiveness of the therapy, but in a quantitative and objective way.

Artificial intelligence, machine learning, big data, radiomics are just some of the many terms that are used by researchers in the field, especially computer scientists, clinical engineers, physicists, to define their activities.

In fact, it will never be sufficient to collect only the data, raw or in DICOM format, also re-elaborated to constitute the so-called big data, but it will be necessary to correlate them with the radiological reports, so that the computer can process them and find the correlation between them and the “phenotype” in question.

Our report will contain over time not only a subjective and qualitative interpretation of the images, our diagnosis, but also a series of numerical values, objective and quantitative features, our contribution to the prognosis, because some of them will have proved useful to predict the efficacy of the therapeutic response.

Artificial Intelligence in Radiology: What you need to know Part 1

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Artificial Intelligence in Radiology: What you need to know Part 2

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