AI in Medical Diagnostic Imaging
Artificial intelligence continues to make strides in medical imaging and diagnostics of certain conditions, such as breast cancer.
A number of years back, Challenger attended an IBM Watson conference. The AI demonstration they were giving at the time was a Watson machine trained to identify basal cell carcinoma from high resolution imaging.
They were interested in whether using Med-Challenger's bank of 28,000 medical Q&A to train their Watson AI would be of benefit. For example, we offer clinical skills CME courses that are image-centric, such as ECG interpretation, radiograph interpretation, and others. We have other special CME courses involving image-based diagnostic questions, like dermatology.
AI image identification in clinical care continues to offer promising results. There have been several Watson-based AI's trained in dermatological lesion identification since then, but what struck us about this version was that they'd had to feed it hundreds of thousands of images for it to reach the accuracy of a dermatologist trained with 50 images.
Of course, the AI advantage is that once you have a trained machine, it is portable and always on. Newer versions of lesion identification by Watson are trained with far fewer images, and the technology and programming having been significantly improved.
AI vs. Human Clinicians? Or team effort?
In Artificial Intelligence, there's the "replacement" camp, which posits that AI will replace people. Then there's the "complementary" camp, that says that human-AI partnerships will far outstrip the performance of either people or AI alone.
New York University released some results from a complementary AI-based ultrasound imaging system that works with humans, but cuts error rates and missed diagnoses almost in half:
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