ChatGPT, Possibility to detect early signs of Alzheimer’s
One day, doctors might be able to use the artificial intelligence algorithms underlying ChatGPT, a chatbot program that has gained notoriety for its capacity to provide human-like written responses to some of the most inventive questions. The GPT-3 algorithm from OpenAI can recognize cues from spontaneous speech that are 80% accurate in predicting the early stages of dementia, according to research from Drexel University’s School of Biomedical Engineering, Science, and Health Systems.
Finding a Sign Early
A thorough assessment of medical history and a battery of physical and neurological examinations and tests are usually performed as part of the standard procedure for diagnosing Alzheimer’s disease today. While there is still no cure for the illness, early detection can provide patients with more therapeutic and support choices. Researchers have been focusing on programs like ChatGPT 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. Language impairment is a symptom in 60-80% of dementia patients.
According to continuing research, Hualou Liang, PhD, a professor in the Drexel University School of Biomedical Engineering, Science and Health Systems and a coauthor of the study, “We know that the cognitive impacts of Alzheimer’s Disease can present themselves in language output.” “In addition to cognitive tests, the most popular methods for early Alzheimer’s diagnosis also examine acoustic characteristics like pausing, articulation, and vocal quality. However, we think that the development of natural language processing software offers a different way to support the early detection of Alzheimer’s.”
A program that listens and gains knowledge
The third iteration of OpenAI’s General Pretrained Transformer (GPT), ChatGPT, employs a deep learning algorithm that was trained by sifting through huge amounts of internet data with a special emphasis on word usage and linguistic construction. This training enables it to generate a human-like response to any language-related task, from answering basic inquiries to producing poetry or essays.
GPT-3 excels in “zero-data learning,” which enables it to reply to inquiries that ordinarily call for outside information that has not been provided. When requesting a software to produce “Cliff’s Notes” on a text, for instance, it is usually necessary to clarify that what is meant by this is a summary. However, GPT-3 has received sufficient training to comprehend the reference and modify to deliver the desired result.
Felix Agbavor, a PhD researcher in the School and the paper’s principal author, said that GPT3’s comprehensive approach to language analysis and production makes it a good contender for finding the subtle speech traits that may predict the start of dementia. GPT-3 would be given the knowledge it needs to extract speech patterns that might later be used to identify indicators in future patients by training it with a vast dataset of interviews, some of which are with Alzheimer’s patients.
Looking for speech cues
The program was trained using a set of transcripts from a subset of a dataset of speech recordings assembled with funding from the National Institutes of Health specifically to test the ability of natural language processing programs to predict dementia. This allowed the researchers to test their theory regarding ChatGPT. The algorithm extracted significant word-use, sentence-structure, and meaning traits from the text to create what experts refer to as a “embedding”—a distinctive profile of Alzheimer’s speech.
The algorithm was subsequently retrained using the embedding, becoming a tool for diagnosing Alzheimer’s disease. To test it, scientists gave the program access to the dataset and asked it to evaluate dozens of transcripts and determine whether or not each one was written by an individual who was developing Alzheimer’s.
The team tested two of the best natural language processing tools side by side and discovered that GPT-3 outperformed both in terms of correctly identifying Alzheimer’s examples, correctly identifying non-examples, Alzheimer’s and with fewer missed cases.
A second test utilized GPT-3’s textual analysis to forecast patients’ scores on the Mini-Mental State Exam, a widely used test for determining the severity of dementia (MMSE).
The accuracy of the GPT-3 forecast was then compared to that of an analysis that predicted the MMSE score only based on the acoustic characteristics of the recordings, such as voice strength, pauses, and slurring. When predicting patients’ MMSE scores, GPT-3 revealed to be about 20% more accurate.
The researchers claimed that their findings “show that the text embedding, created by GPT-3, can be reliably utilized to not only distinguish persons with Alzheimer’s Disease from healthy controls, but also to estimate the subject’s cognitive assessment score, both entirely based on speech data.” “We further demonstrate that text embedding performs better than the traditional acoustic feature-based method and even competes with tuned models. These findings collectively imply that GPT-3 based text embedding is a promising method for assessing AD and has the potential to enhance dementia early diagnosis.”
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