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'Lazy' AI: UW researchers find that tech can misdiagnose COVID-19 by taking shortcuts


‘Lazy’ AI: UW researchers find that tech can misdiagnose COVID-19 by taking shortcuts
June 3, 2021 at 12:30 pm
Left to right: University of Washington researchers Alex DeGrave, Su-In Lee and Joseph Janizek. (University of Washington Photo)
The future use of artificial intelligence in medical contexts could be beneficial in improving efficiency, but a new University of Washington research study published in Nature found that AI relied on shortcuts rather than actual medical pathology in diagnosing COVID-19.
The researchers examined chest X-rays used to detect COVID-19. They found that the AI relied more on specific datasets than significant medical factors to predict whether a patient had contracted the virus. ....

Alex Degrave , Joseph Janizek , University Of Washington , Su In Lee , பல்கலைக்கழகம் ஆஃப் வாஷிங்டன் , சு இல் லீ ,

Medical AI models rely on 'shortcuts' that could lead to misdiagnosis of COVID-19


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Artificial intelligence promises to be a powerful tool for improving the speed and accuracy of medical decision-making to improve patient outcomes. From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today.
But as University of Washington researchers discovered, AI models like humans have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.
In a new paper published May 31 in
Nature Machine Intelligence, UW researchers examined multiple models recently put forward as potential tools for accurately detecting COVID-19 from chest radiography, otherwise known as chest X-rays. The team found that, rather than learning genuine medical pathology, these models rely instead on shortcut learning to draw spurious associat ....

Gabriel Erion , Pascal Sturmfels , Scott Lundberg , Alex Degrave , Joseph Janizek , Microsoft Research , University Of Washington , Uw Medical Scientist Training Program , National Institutes Of Health , National Science Foundation , Allen School , Nature Machine Intelligence , Computer Science , Scientist Training , Su In Lee , Nature Machine , National Institutes , Health Care Systems Services , Infectious Emerging Diseases , Technology Engineering Computer Science , Computer Science , Software Engineering , Robotry Artificial Intelligence , கேப்ரியல் ஏரிஓன் , ஸ்காட் லண்ட்பெர்க் , மைக்ரோசாஃப்ட் ஆராய்ச்சி ,