Traditional methods of diagnosing mental-health conditions require patients to speak directly to a psychiatrist. Sensible in theory, such assessments can, in practice, take months to schedule and ultimately lead to subjective diagnoses.
That is why scientists are experimenting with ways to automate this process. Artificial-intelligence (AI) tools trained to listen to patients have proved capable of detecting a range of mental-health conditions, from anxiety to depression, with accuracy rates exceeding conventional diagnostic methods.
By analysing the acoustic properties of speech, these AI models can identify markers of depression or anxiety that a patient might not even be aware of, let alone able to articulate. Though individual features like pitch, tone and rhythm each play a role, the true power of these models lies in their ability to discern patterns imperceptible to a psychiatrist’s ears.
For overburdened clinicians, speech analysis conducted by AI models could help triage patients and offer continuous monitoring for those requiring at-home treatments https://t.co/3XzKx6IR04
— The Economist (@TheEconomist) October 4, 2024
Illustration: Mark Pernice pic.twitter.com/Ct639aO9Yr
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