Ramsey Wehbe

Dr. Ramsey Wehbe, a cardiologist and postdoctoral fellow in A.I.[1]Wehbe is a postdoctoral fellow at Bluhm Cardiovascular Institute at Northwestern Memorial Hospital.[2]Wehbe, MD, in the midst of our Northwestern Medicine Fellowship in Artificial Intelligence in Cardiovascular Disease, recently published a paper with impressive results from a set of 5,853 patients using machine learning to accurately identify COVID-19 using chest X-rays.[3]Wehbe and his colleagues expect the system to save not just on time but the costs associated with employing radiologists to identify potential COVID-19 patients.[4]

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Recent events

New A.I. is fast and accurate at detecting COVID-19 from chest X-rays

The breakthrough will likely be instrumental in preventing the spread of the virus, particularly amongst hospital patients and staff, by providing a rapid and accurate method for screening patients admitted to hospital either with or without COVID-19 symptoms by merely scanning their chest x-rays. The technology will act as an early warning system, highlighting those who need to self-isolate even before symptoms develop and, additionally, picking up on those who may have never been aware of their need to isolate.[1]


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AI detects COVID-19 on chest X-rays with accuracy and speed

Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The study's authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19. Katsaggelos' laboratory specializes in using A.I. for medical imaging. He and Wehbe had already been working together on cardiology imaging projects and wondered if they could develop a new system to help fight the pandemic.[2]


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3 hospital execs: How to ensure medical AI is trained on sufficiently diverse patient data

The panel said a lack of transparency surrounding the datasets that train algorithms can lead to public mistrust in AI-powered medical tools, as these devices may not have been trained using patient data that represents the patients on which they will be used.[3]


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