Health Medical

AI will soon diagnose illnesses based on speech patterns

HQ Team

October 11, 2022:Your voice can reveal a lot, including any illnesses you suffer from. The National Institutes of Health is funding research to develop an Artificial Intelligence-based tech that could diagnose people based on their speech.

“Everything from your vocal cord vibrations to breathing patterns when you speak offers potential information about your health,” says laryngologist Dr Yael Bensoussan, the director of the University of South Florida’s Health Voice Center and team lead.

“We asked experts: Well, if you close your eyes when a patient comes in, just by listening to their voice, can you have an idea of the diagnosis they have?” Bensoussan says. “And that’s where we got all our information.”

Some of the revealing symptoms are already known. Slurring of speech is a sign of a stroke. Slow speaking with low audibility is associated with Parkinson’s disease. Your speech pattern can also reveal cancer and depression. 

The research project aims to collect the voices of people with conditions in five areas: neurological disorders, voice disorders, mood disorders, respiratory disorders and pediatric disorders like autism and speech delays.

The project was launched over a year ago with more than $100 million in federal government funding, creating large-scale health care databases for precision medicine.

“We were really lacking large what we call open source databases,” Bensoussan says. “Every institution kind of has its own database of data. But to create these networks and these infrastructures was really important to then allow researchers from other generations to use this data.”

 AI has often been used to detect speech patterns, but this is the first time such large-scale data collection has been initiated. This project is a collaboration between USF, Cornell and 10 other institutions.

“We saw that everybody was kind of doing very similar work but always at a smaller level,” Bensoussan says. “We needed to do something as a team and build a network.”

The project researchers intend to build an app that could help physicians in rural and inaccessible communities to aid in diagnosis and refer patients to specialists sooner if needed. 

For such an app to be successful and feasible, large-scale data collection is essential, as all AI tools are as good as the input. By the end of the four years, the researchers hope to collect about 30,000 voices, with data on other biomarkers — like clinical data and genetic information — to match.

“We really want to build something scalable,” Bensoussan says, “because if we can only collect data in our acoustic laboratories and people have to come to an academic institution to do that, then it kind of defeats the purpose.”

AI in health and legalities

There are some other legal hurdles to be crossed, as voice data collection can become a breach of medical privacy. “Let’s say you donate your voice to our project,” says Yael Bensoussan. “Who does the voice belong to? What are we allowed to do with it? What are researchers allowed to do with it? Can it be commercialized?”

Digitization of diagnostic processes helps make treatment more accessible, accurate, and cost-effective in the long run.

“The right treatment for the right patient at the right moment—that is what personalized medicine means for me,” says Dr Yacine Hadjiat, Global Head of Digital Solutions at Biogen Health (BDH). Hadjiat primarily focuses on technical applications. “AI-powered imaging solutions, smartphones, smartwatches, and other wearables equipped with sensors gather data to enable treatment to be tailored precisely to the individual patient. Motion, speech, writing and reading habits—all these things function as digital biomarkers.”

Cons of AI in healthcare

But there are some concerns about the digitization and automation of public healthcare. “My fear is we will end up with what I’ve been calling a ‘health care apartheid,'” says Sonoo Thadaney Israni at the Stanford University medical school. “If we create algorithmic care and ‘kiosk’ it in some fashion — focusing on efficiency and throughput — the people who will end up having access and using it will be the ones who already lack privileges of various kinds.”

Cost saving and reach should not be at the cost of care versus profitability. In fact, the cost-savings these technologies promise could be the result of reducing the time an individual spends face-to-face with a doctor or nurse.

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