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The artificial intelligence algorithms (算法) behind the program of an AI chat app, which has drawn attention for its ability to generate humanlike written responses to some of the most creative queries, might one day be able to help doctors detect Alzheimer’s disease in its early stages. Research from Drexel University’s School of Biomedical Engineering, Science and Health Systems recently proved that the program of the AI chat app can identify clues from spontaneous (自发的) speech that are 80% accurate in predicting the early stages of dementia (痴呆) .

The current practice for diagnosing Alzheimer’s disease typically involves a medical history review and lengthy set of physical and neurological evaluations and tests. While there is still no cure for the disease, spotting it early can give patients more options for ways of treatment and support. Because the language disability is a symptom (症状) in 60%-80% of dementia patients, researchers have been focusing on programs that can pick up on unobvious clues, such as hesitation, making grammar and pronunciation mistakes and forgetting the meaning of words, as a quick test that could indicate whether or not a patient should have a full examination.

“The AI systemic approach to language analysis and production makes it a promising possibility for identifying the unobvious speech characteristics that may predict the beginning of dementia,” said Felix Agbavor, a doctoral researcher in the school and the lead author of the paper. “Training the app with a lot of data of interviews, some of which are with Alzheimer’s patients, would offer the information it needs to select speech patterns that could then be applied to identify markers in future patients.”

To build on these promising results, the researchers are planning to develop a web application that could be used at home or in a doctor’s office as a pre-screening tool.

“Our proof-of-concept shows that this could be a simple, accessible and fully sensitive tool for community-based testing,” Felix said. “This could be very useful for early screening and risk assessment before a clinical diagnosis.”