AI News, VIDEO: Artificial Intelligence in Cardiac Imaging

Medical imaging and artificial intelligence: naturally compatible

'As an experimental psychologist, I have always been interested in trying to discover why images are viewed and interpreted differently by different people,' says Elizabeth Krupinski, professor and vice chair for research in the Department of Radiology and Imaging Sciences at Emory University in Atlanta, Georgia.

Throughout her career in radiology, she has conducted numerous studies assessing the impact of how image data are presented to the radiologist, and how the addition or change in data format or content can impact diagnostic efficacy and efficiency.

Projects include not only the more traditional use of AI to detect lesions, and segment and measure images, but also exploring ways to use AI to go beyond detection and provide more diagnostic information by combining imaging with related data from the electronic medical record;

The potential of pigeons In a 2016 SPIE Proceedings article, 'The potential of pigeons as surrogate observers in medical image perception studies,' Krupinski writes about using pigeon models 'as a surrogate for the human observer.'

Despite the intriguing title, the goal of the study wasn't to use pigeons to diagnose images clinically, but rather to gain an understanding of how visual learning takes place, and which types of visually learned tasks generalize well to some applications, but not to others.

Krupinski explains that the insight this study provides to those developing or using AI in medical imaging is two-fold: 'On the one hand, it speaks to the fact that each image interpreter - human, computer, or animal - ‘sees' the image data in a different way, and thus performs differently at a given task,' she says.

'If AI can relieve clinicians of redundant, time-consuming tasks (such as measuring size changes in lesions over time with treatment), that are readily and often more accurately and consistently done by computers, clinicians will have more time to dedicate to the actual decision-making process and to interact with patients.'

She noted that while AI will definitely revolutionize healthcare and thereby medical physics, it's imperative to understand that it will not (or perhaps should not) do what many are afraid it will - take over the roles and responsibilities of the doctor, radiologist, or other medical professionals.

Krupinski points out that while AI can certainly be used to develop and provide a variety of training tools, it cannot sit down with a trainee, listen to their problems, explain subtle concepts and the 'art' of medical physics, or provide them with the mentorship and guidance and support required to foster their success as independent professionals.

Krupinski notes that a significant portion of a medical professional's job - whether solving a complicated clinical problem, developing a new line of research investigation, or communicating and collaborating with colleagues and patients - involves creativity and ingenuity.

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