The future of AI in radiology
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The future of AI in radiology

January 12, 2018
From the November 2017 issue of HealthCare Business News magazine

For the tuberculosis example, the level of accuracy would certainly benefit numerous patients in many parts of the world, if immediately deployed. Indeed, it is in areas where access is poor and specialists are scarce that AI will initially have the biggest impact. But deployment of these algorithms is an ongoing challenge, with data privacy and security being critically important, as well as issues of data provenance and data transfer. Whether deployment is to the cloud, to on-premise data centers or to edge devices, there are benefits and tradeoffs to consider for each.

How do we allow interactive probing of the model to intuit its inner workings? How do we deliver continuous updates or improvements à la websites or apps? How do we implement guard rails or heuristics to plan for the expected failure cases? What is clear is that we need the ability to rigorously validate on new patient data: a common issue in machine learning--particularly deep learning--is overfitting on the training data such that performance on new unseen data is poor. As AI model complexity is continuously pushed to ever-higher levels, “evidence-based” becomes not just desired, but fundamental.

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The current state of AI in radiology has never before looked this promising. We should try to reign in natural tendencies to overhype, lest we overinflate our expectations and slide once again into a trough of disillusionment. We should continue to expand the realm of algorithmic possibilities, while at the same time be mindful that the processes and infrastructure to support these algorithms are vitally important as well. Patient outcomes will no doubt be improved, but it will require the efforts of an entire community and a thriving ecosystem--no single person, institution, or company can tackle any piece alone

About the author: Leon Chen is the co-founder and CEO of MD.ai. He is an engineer and physician with an M.D. from Harvard Medical School and extensive background in machine learning.

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