Artificial intelligence (AI) continues to grow as machine learning expands its capabilities into new industries. At ECR 2018, artificial intelligence was a theme on opening day as Professor Wiro Niessen from Biomedical Imaging Group Rotterdam, the Netherlands, addressed AI in the press conference. Declaring AI an enormous opportunity for the field, Niessen confirmed his standpoint that the radiological community must work together with those in machine learning in order to ensure that AI has a positive impact on medicine.
Although AI will undoubtedly change the radiological field, Niessen wants to calm concerns that computers will replace humans, saying that human intelligence will complement artificial intelligence in radiology and medical imaging.
Yet, there is a long way to go before we see the real impact of AI in medicine. Referencing a statement that Aliah Sohani made at the RSNA last November, Niessen commented on how the speed of change tends to be overestimated and the impact underestimated. Niessen concedes that ultimately the field needs more years of collecting reliable data in order to train the algorithms for AI to improve.
“There's a long way to go, and it takes a while before these new technologies really have an improvement in daily clinical practice,” Niessen says. “In the short term, yes there is not so much that is really improving the radiologist's daily work, but it will come, and the impact will be very immense.”
He goes on to cite the biggest hurdle to adapting AI for medical imaging is making sure those developing AI are seeking solutions that actually help in the daily clinical workflow of radiologists to improve diagnostics and prognostics. Another large hurdle to the technology’s adoption is ensuring that the machine learning algorithms are able to work in different situations and hospitals, and are able to explain why the algorithms came to a certain decision.
Niessen references his own work environment in Rotterdam as a solution to overcoming hurdles in adoption. His team of people working on machine learning and AI are located within the hospitals, directly collaborating with clinical research and radiologists. This collaboration provides access to testing prototypes and receiving feedback to improve the value of imaging for patients.
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