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WebMD Health News


CHATGPT'S AI COULD HELP CATCH ALZHEIMER'S EARLY

Written by Denny Watkins



Feb. 3, 2023 -- The artificial intelligence that can write essays and pass tests
can also help identify dementia.

Researchers at Drexel University in Philadelphia used the AI behind ChatGPT
(which has grabbed headlines for writing believable term papers and passing bar
exams) to analyze speech, and the system correctly identified Alzheimer’s
patients 80% of the time, according to the study published in the journal PLOS
Digital Health.

The researchers used GPT-3, the language model that drives ChatGPT, to analyze
audio clips of people describing a picture in a standard test for dementia. 

Alzheimer’s patients often repeated themselves, strayed from describing the
picture’s contents, didn’t finish thoughts, and referred to objects vaguely as a
“thing” or “something.”

“GPT-3 is able to capture such a subtle difference reflected in the text,” says
study author Hualou Liang, PhD, professor of biomedical engineering at Drexel. 



The software analyzed text transcribed (also by software) from 10-second
recordings of healthy adults and Alzheimer’s patients. The text trained the
GPT-3 model to identify the subtle differences between regular language and
speech from someone experiencing cognitive decline.

The GPT-3 machine learning models understand passages of text by converting
words into mathematical representations called “embeddings.” The embeddings are
multi-dimensional signals, which allow the AI to identify subtle differences and
similarities that even experienced doctors can’t hear. GPT-3 compares the text
passages by measuring the distance between those signals in the embeddings. 

Because GPT-3 only analyzes written text, the process bypasses the pauses and
other sounds in spoken language that aren’t words. In this case, that turned out
to be an advantage: The GPT-3 analysis outperformed some machine learning models
developed by other laboratories that included those sounds.

Other studies, however, have found that the “ahs” and “ums” in speech can be
important in revealing Alzheimer’s. A 2021 study that encoded those pauses
allowed a machine learning model to detect Alzheimer’s disease with 90%
accuracy, and a separate study conducted in Slovenia that combined text and
acoustic features achieved an accuracy of 94%.

“The best combination tends to be combining both types of features together,”
says Frank Rudzicz, PhD, associate professor of computer science at the
University of Toronto. “There’s a lot of information in the words and structure
of the transcripts, but also in our tone of voice.”

Using Voice to Spot Alzheimer’s

More and more researchers are looking at voice as a biomarker, a way to detect
various diseases including Alzheimer’s. 

Worldwide, Alzheimer’s cases are successfully detected just 48% of the
time, according to estimates by the World Health Organization. Higher-income
countries achieve a 54% diagnostic rate, while low- and middle-income countries
are only identifying 24% of Alzheimer’s cases.

Researchers in this field hope to close that gap by developing a tool that can
detect Alzheimer’s early — when the effects may be too subtle for a physician to
notice. “There is no cure for Alzheimer’s disease yet, but there are life
changes that can delay some of its effects, so early diagnosis is still
important,” says Rudzicz, who co-founded a speech analytics mobile app
called Winterlight. “These kinds of technologies could also be applied to other
disorders, including Parkinson’s, depression, and so on.”

Doctors could eventually use a device or computer program to test a patient’s
cognitive abilities in their office. Brain scans or other clinical tests could
then confirm the Alzheimer’s diagnosis.

Another application might use smart devices like Alexa and Siri to monitor your
regular conversations (with your consent) and alert you if it notices any
worrying word fumbles. It may even detect other psychological problems like
depression and stress. 

“The analysis could be done in a privacy-preserving manner once the system is
fully functional,” says Liang. “As such, it could make an immediate and
significant impact on mitigating the dementia problem in the older adult
community.”



SHOW SOURCES

SOURCES: 

Hualou Liang, PhD, professor of biomedical engineering, Drexel University,
Philadelphia.

Frank Rudzicz, PhD, associate professor of computer science, University of
Toronto.

Saturnino Luz, PhD, medical informatics researcher, University of Edinburgh.

PLOS Digital Health: “Predicting dementia from spontaneous speech using large
language models.”

Frontiers in Computer Science: “Pauses for Detection of Alzheimer’s Disease,”
“Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's
Diagnosis Based on Spontaneous Speech.” 

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