Research in Neuroscience suggests that indicators of Alzheimer’s disease could be detected early: a subtle change in speech patterns, grammatical mistakes, forgetting the meaning of a word, or mispronouncing common words that used to flow naturally.
A team from Drexel University took a major step in applying GPT-3’s capacity to detect text-patterns, using a massive dataset of interviews that included patients with and without Alzheimers. Based on this approach, information needed to extract speech patterns that could be applied to identify markers of Alzheimers in future patients is now available.
“This could be very useful for early screening and risk assessment before a clinical diagnosis”, one of the authors of the study suggests.
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