Diagnostics
transforming whatever field it is applied to, but also a potential threat. We are a long way from the autonomous robots of science fiction, but if machines keep learning, what is the end result? What happens when a machine surpasses human expertise? Medicine remains one of the most contentious fields where AI is concerned, because it occupies this knife edge more overtly than any other. When used properly, AI has the potential to save lives in great swathes; but when a wrong decision can result in harm and even fatality, what happens to accountability? And who gets the last word, human or machine?
Neural networks aiding more rapid diagnoses
Kimmo Kartasalo, a researcher at the department of medical epidemiology and biostatistics at Karolinska Institutet, has been working on a study that looks at the use of AI to accurately diagnose prostate cancer. “Our study features a collection of AI algorithms contributed by more than 1,000 developers across the world,” Kartasalo explains. “These methods are based on deep neural networks, a widely used family of AI algorithms. During the training phase, the deep neural networks are fed very high-resolution images of prostate tissue obtained during a prostate biopsy, digitised with a digital pathology scanner. The algorithms are also provided with the diagnoses performed by expert pathologists, and during the training process the deep neural network is optimised to replicate the pathologists’ diagnosis as accurately as possible. Once trained, these algorithms can be used to process new prostate biopsies and predict whether the biopsy contains cancer tissue, and to estimate the severity of the disease on the Gleason grading scale.”
The promise of such a technology cannot be understated. As Kartasalo explains, these algorithms “could complement the expertise of human experts by providing decision support to reduce the variation in prostate cancer grading, to save time and to improve patient safety and diagnostic quality.” Anything that aids in a faster and more accurate diagnosis of cancer must be a good thing – and yet, there is something jarring about the use of terms like ‘learning’ and ‘training’ to describe a piece of software, as if these technologies were quietly sentient.
A pathway into less-invasive processes Such semantics pervade studies on artificially intelligent developments in the medical field. Aydogan Ozcan, chancellor’s professor and volgenau chair for engineering innovation of the electrical and computer engineering department at UCLA Samueli, is the senior author on a study that looks at the use of imaging technology to reduce need for skin biopsies. “The process that we developed in this paper,” Ozcan explains, "bypasses several standard
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steps typically used for diagnosis, including skin biopsy, tissue fixation, processing, sectioning and histochemical staining.” To achieve this, Ozcan and his team “created a deep learning framework to transform images of intact skin acquired by an emerging non-invasive optical technology, reflectance confocal microscopy (RCM), into a format that is user-friendly for dermatologists and pathologists.” Through the application of this technology, Ozcan hopes to bypass the “special training” that is currently required to analyse RCM images. “Our approach,” Ozcan says, “will be a major step forward for clinical practice within dermatology.” Though not at clinical use stage yet, Ozcan predicts this technology will likely have widespread application. “There are several steps remaining in translating this technology for clinical use,” he says, “and our goal is to provide virtual histology technology that can be built into any device – large, small or combined with other optical imaging systems. Once the neural network is trained with many tissue samples and the use of powerful graphics processing units (GPUs), it will be able to run on a computer or network, enabling rapid transformation from a standard image to a virtual histology image.” There is no doubt about it, such a technology would be significantly transformative – but one wonders how these “trained” networks will impact on the future of trained dermatologists.
BoneView detecting fractures on several X-rays.
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Radiology: Volume 302: Number 3
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