AI may be used to screen for Alzheimer’s disease by analysing speech. A team from IBM and Pfizer says it has trained AI models to spot early signs of the notoriously difficult to detect degenerative disease Alzheimer’s by looking at linguistic patterns in word usage. This method stands out because it used historical information from the multigenerational Framingham Heart Study, which has been tracking the health of more than 14,000 people from three generations since 1948. Other researchers have already trained various models to look for signs of cognitive impairments, including Alzheimer’s, by using different types of data, such as brain scans and clinical test results. If this new models’ ability to pick up trends in such data holds up in forward-looking studies of bigger and more diverse populations, researchers say they could predict the development of Alzheimer’s a number of years before symptoms become severe enough for typical diagnostic methods to pick up, such a screening tool would not require invasive tests or scans. The model achieved 70% accuracy in predicting which of the Framingham participants eventually developed dementia associated with Alzheimer’s disease before the age of 85. This result was based on historical data rather than actually predicting future events and the study has been further limited by not having a diverse population set. Adding the studying of the test-subject’s handwriting in the next generation of the AI could dramatically improve the model further as it could then pick up traits like evidence of tiny tremors, switching between print and cursive, and very tiny letters which are all closely associated to Alzheimer’s.