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.<\/p>\n\n\n\n
That is why scientists are experimenting with ways to automate this process. Artificial-intelligence (AI<\/small>)<\/a> 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.<\/p>\n\n\n\n By analysing the acoustic properties of speech, these AI <\/small>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\u2019s ears.<\/p>\n\n\n\n Read it all<\/a>.<\/p><\/p> 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<\/a>
Illustration: Mark Pernice pic.twitter.com\/Ct639aO9Yr<\/a><\/p>— The Economist (@TheEconomist) October 4, 2024<\/a><\/blockquote>