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Artificial Intelligence models like humans have a tendency to look for shortcuts and in the case of an AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings, warn researchers.
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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 associations between me
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IMAGE: PD Dr. Rickmer Braren (l.) und Prof. Daniel Rueckert (r.) exploring diagnostic possibilities using artificial intelligence for medical image data. view more
Credit: Andreas Heddergott / TUM
Digital medicine is opening up entirely new possibilities. For example, it can detect tumors at an early stage. But the effectiveness of new AI algorithms depends on the quantity and quality of the data used to train them.
To maximize the data pool, it is customary to share patient data between clinics by sending copies of databases to the clinics where the algorithm is being trained. For data protection purposes, the material usually undergoes anonymization and pseudonymization processes - a procedure that has also come in for criticism. These processes have often proven inadequate in terms of protecting patients health data, says Daniel Rueckert, Alexander von Humboldt Professor of Artificial Intelligence in Healthcare and Medicine at TUM.