Can the AI Driving ChatGPT Help To Detect Early Signs of Alzheimer's Disease?

The artificial intelligence algorithms behind the chatbot program ChatGPT -- which has drawn attention for its ability to generate humanlike written responses to some of the most creative queries -- might one day be able to help doctors detect Alzheimer's Disease in its early stages. EurekaAlert: Research from Drexel University's School of Biomedical Engineering, Science and Health Systems recently demonstrated that OpenAI's GPT-3 program can identify clues from spontaneous speech that are 80% accurate in predicting the early stages of dementia. Reported in the journal PLOS Digital Health, the Drexel study is the latest in a series of efforts to show the effectiveness of natural language processing programs for early prediction of Alzheimer's -- leveraging current research suggesting that language impairment can be an early indicator of neurodegenerative disorders.

The current practice for diagnosing Alzheimer's Disease typically involves a medical history review and lengthy set of physical and neurological evaluations and tests. While there is still no cure for the disease, spotting it early can give patients more options for therapeutics and support. Because language impairment is a symptom in 60-80% of dementia patients, researchers have been focusing on programs that can pick up on subtle clues -- such as hesitation, making grammar and pronunciation mistakes and forgetting the meaning of words -- as a quick test that could indicate whether or not a patient should undergo a full examination.

GPT-3, officially the third generation of OpenAI's General Pretrained Transformer (GPT), uses a deep learning algorithm -- trained by processing vast swaths of information from the internet, with a particular focus on how words are used, and how language is constructed. This training allows it to produce a human-like response to any task that involves language, from responses to simple questions, to writing poems or essays. GPT-3 is particularly good at 'zero-data learning' -- meaning it can respond to questions that would normally require external knowledge that has not been provided. For example, asking the program to write 'Cliff's Notes' of a text, would normally require an explanation that this means a summary. But GPT-3 has gone through enough training to understand the reference and adapt itself to produce the expected response. Link to the paper.

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