Like other subspecialties in medicine, Radiology faces its own unique set of ongoing challenges. From decreasing reimbursement for radiology reports and procedures to increasing physician burnout, there are many pressing issues in the radiology field.
However, the most critical threat to radiology is considered to be the advent of deep learning and computer vision. The idea of having a black box, where a medical image is analyzed at one end and a full radiology report is produced at the other end, is now universally considered to be the most significant threat to the radiology profession. To best determine how radiology as a field should react to the emergent field of machine learning, the first step is to cultivate a better understanding of the fundamental technology. This is why leadership of the Pennsylvania Roentgen Ray Society (PRRS) decided to hold one of their events around artificial intelligence (AI) and the implications it holds for the future of radiology.
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It was fitting that the event to discuss the future of medical imaging was held at the historical Mütter Museum College of Physicians in Philadelphia, where the history and relics of medicine are showcased. The interest among the radiology community was quite palpable—all the seats were taken and the event was quickly standing room only.
The session was coordinated by the leadership of the Pennsylvania Roentgen Ray Society: Dr. Valeria Potigailo, Dr. Tessa Cook, Dr. Beverly Hershey, and Dr. Ryan Lee. Dr. Paras Lakhani, from Jefferson University Hospitals, delivered the keynote speech, “Strengths and Limitations of Deep Learning in Medical Imaging.” He provided an outstanding overview of the history of deep learning that led naturally into its applications within medical imaging. Dr. Lakhani’s talk masterfully captured the double-edged sword of deep learning in radiology, highlighting powerful applications and the inherent weaknesses for all such models.
As he discussed the current machine learning research for use cases in radiology, Dr. Lakhani no doubt simultaneously excited the data scientists and frightened the radiologists in the crowd. Particular neural networks are approaching human performance levels in detecting malignant or benign growths in images such as mammograms. For dealing with low quality CT image scans, different neural networks called autoencoders have demonstrated the ability to boost image quality by generating similar images with “repaired” pixel values, which the network has learned from training on similar data.
Finally, while the use of machine learning in radiology is still limited due to the relatively small number of high-quality, labeled datasets, the use of transfer learning opens up a near-term path forward. Neural networks that are trained on large, existing image datasets are able to “transfer” learned patterns (such as basic shapes, edges, etc.) readily to new datasets in more targeted fields, such as radiology. These transfer networks show dramatic increases in accuracy compared to networks trained on the target data alone.
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But as impressive as AI’s potential within radiology is, Dr. Lakhani highlighted several critical studies which lay bare the current weaknesses of these seemingly impressive models.