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Latest medical research finds that artificial intelligence can detect type 2 diabetes merely by listening to a patient speak for six to 10 seconds.
The study by Klick Labs, published in “Mayo Clinic Proceedings: Digital Health,” showed an 89% accuracy rate for diagnoses in women and 86% for men, in line with a release detailing the breakthrough.
“Our research highlights significant vocal variations between individuals with and without Type 2 diabetes and will transform how the medical community screens for diabetes,” said first creator Jaycee Kaufman.
“Current methods of detection can require a whole lot of time, travel, and price. Voice technology has the potential to remove these barriers entirely,” Kaufman continued.
Researcher had 267 individuals — who did or didn’t have type 2 diabetes — record phrases on their smartphones six times a day for 2 weeks. Greater than 18,000 recordings were analyzed for over 14 different acoustic features, which were different amongst diabetics and non-diabetics. Participants also provided basic health data like age, height, and weight.
Signal processing technologies were capable of perceive certain notes of vocal pitch that don’t register with the human ear. These hidden sounds provided the vital clues, in line with Kaufman.
“Our research underscores the tremendous potential of voice technology in identifying Type 2 diabetes and other health conditions,” Klick VP and principal investigator Yan Fossat said.
“Voice technology could revolutionize healthcare practices as an accessible and reasonably priced digital screening tool.”
The subsequent step for Klick is replicating the study and expanding the vocal search to search for pre-diabetes, hypertension and more.
This news follows a recent MIT breakthrough of a bio-implant which is capable of mold itself more seamlessly to the body, aiding within the deployment of medicines reminiscent of insulin.
