Artificial intelligence is quietly transforming the way mental health diagnoses are made. A research team from Kaunas University of Technology has developed a revolutionary model for diagnosing depression, utilizing multimodal analysis of voice and EEG data to pave the way for accurate identification of mental health issues.
The core of this research lies in breaking the limitations of traditional single-data diagnostics. The team chose voice as a key data source because it can subtly reflect emotional states. Factors such as speech rate, tone, and emotional energy may serve as potential indicators of depression.
Image source note: Image generated by AI, image licensed by service provider Midjourney
By converting EEG and voice data into visual spectrograms, the research team employed an improved deep learning model, ultimately achieving an astonishing 97.53% accuracy in depression diagnosis. This suggests that AI may offer more objective and precise tools for mental health diagnosis in the future.
Professor M. Kliunas, the research leader, admitted that there are still challenges ahead for the future development of this technology. One of the next hurdles to overcome is how to enable AI not only to provide diagnostic results but also to explain the basis for those diagnoses.
Even more thought-provoking is how this research reflects the enormous potential of AI in the healthcare field. While protecting patient privacy, leveraging technology to provide more precise interventions for mental health may become a significant direction for future medical technology.
Depression currently affects 28 million people globally each year, and the emergence of AI may bring timely and accurate diagnostic hope to countless patients.
