Is it problematic to use AI to predict Parkinson’s?

Allder’s main concern about using machine-learning technology to predict diseases such as Parkinson’s revolved around the accessibility of such a tool for people who need it.

“I don’t foresee problems with the AI model, but I do foresee problems with patients accessing it,“ he told us.

“While AI models are powerful tools for identifying disease subtypes and predicting progression, there are potential issues related to patient access. Not all patients may have access to advanced diagnostic tools or treatments derived from AI research, especially in under-resourced settings,“ Allder pointed out.

However, according to him, another issue might be “[t]he use of extensive patient data for AI model training,“ which “raises concerns about data privacy and security.“

“AI models need to be validated across diverse populations to ensure they are not biased towards specific cohorts,” said Allder.

Scherzer, echoing his earlier statement, said that the significant power of artificial intelligence toward precise medical treatments will ultimately depend on more research and trials.

“The success of AI to predict outcomes depends on the size and quality of the input data,” he noted. “A key gap in the field is that we need much larger, high quality, longitudinal data sets of Parkinson’s patients — data on large populations spanning prodromal stages and the entire disease course. These will be essential for training and validating AI models useful for augmented medicine.”