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AI reads faces to detect stroke with up to 98% accuracy

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AI reads faces to detect stroke with up to 98% accuracy
Photo by 本草圈 / Unsplash

What if a computer could tell you were having a stroke just by looking at your face? A new review of nine studies suggests facial expression recognition (FER) technology might do just that, with accuracies ranging from 82 to 98% for diagnosing stroke.

The technology also shows promise for monitoring rehabilitation. One study found it could track how intensely a patient was working in therapy with 99.81% accuracy. That could help therapists adjust exercises in real time.

But before you expect this at your local hospital, there are big caveats. The review included only nine studies out of 1,855 identified, and the researchers note that FER models face considerable challenges in real-world clinical translation. Lighting, camera angles, and individual facial differences can trip up the algorithms.

Still, the potential is clear. For stroke patients, faster diagnosis and better rehab monitoring could mean better outcomes. For now, this technology remains an auxiliary tool, not a replacement for standard care.

What this means for you:
Facial recognition AI shows high accuracy for stroke diagnosis and rehab monitoring, but real-world use is still challenging.
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