Advances in artificial intelligence are rapidly changing how people interact with technology, from voice assistants to medical imaging.
Now, researchers are finding that the same tools could help doctors detect and classify neurodegenerative conditions such as Alzheimer’s and Parkinson’s disease more accurately and at earlier stages.
This shift could have major implications for how clinicians assess cognitive decline and provide care.
A new study published in the Journal of Neuropsychology (2025), explores how artificial intelligence, and particularly machine learning algorithms, are being applied to neuropsychology.
The narrative review focuses on their use in cognitive assessment for patients with neurodegenerative disorders.
The authors report that machine learning systems can analyze large volumes of test results and biological markers with a level of precision beyond traditional approaches.
These methods can identify subtle differences in brain function and behavior that may help distinguish between conditions such as mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, and primary progressive aphasia.
Neurodegenerative disorders are a group of conditions characterized by gradual loss of brain function, often affecting memory, language, or motor skills.
Diagnosing these conditions is challenging because symptoms may overlap, progress differently across individuals, and resemble those of normal aging.
Traditional neuropsychological assessments rely on standardized tests and clinical judgment, which, while valuable, can miss early signs or subtle distinctions.
The research team reviewed studies in which artificial intelligence models were trained to classify patients using data such as eye movements, speech patterns, and results from neuropsychological tasks.
Algorithms including support vector machines, random forests, and neural networks were able to detect meaningful differences with impressive accuracy.
For example, eye-tracking data during reading tasks could identify people with mild cognitive impairment with more than 70 percent accuracy, while voice analysis helped distinguish between stages of Parkinson’s disease with over 90 percent accuracy in some cases.
These findings suggest that machine learning can enhance diagnostic precision by integrating behavioral signals that are often overlooked in standard testing.
By combining voice, gaze, and other markers with clinical test data, algorithms may offer clinicians additional tools to interpret complex cases and monitor disease progression.
The approach also has potential for use in remote or low-cost screening, since many of these markers—such as speech—can be collected through everyday devices like smartphones.
For the general public, the relevance lies in the possibility of earlier and more accurate detection of cognitive disorders.
Early diagnosis is critical because it can improve access to treatment, support planning for care, and reduce uncertainty for patients and families.
If artificial intelligence can reliably pick up on subtle changes years before traditional assessments, it may change how people understand and respond to cognitive health concerns.
At the same time, the authors emphasize that these technologies are not without challenges.
Many machine learning systems require large, high-quality datasets to perform well, and results may not always generalize across diverse populations.
Overfitting—a situation where an algorithm learns patterns specific to one dataset but fails to apply them more broadly—remains a concern.
There are also questions about how to integrate AI results into clinical decision-making without replacing the expertise and judgment of trained professionals.
Future research will need to address these issues, as well as explore the ethical implications of using automated systems in healthcare.
Transparency, patient consent, and data privacy will be critical if AI-based tools are to be adopted widely.
Nonetheless, the review highlights the growing evidence that machine learning can provide new insights into brain function and cognitive decline, with potential benefits for both clinical practice and scientific understanding.
Artificial intelligence is unlikely to replace human clinicians in neuropsychology, but it may become an important partner in the diagnostic process.
By offering a new layer of analysis and helping to capture the complexity of neurodegenerative conditions, these technologies could contribute to more accurate, efficient, and accessible care.
Citation
Scandola, M., Esposito, M., Guidotti, R., & Romano, D. (2025). How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders. Journal of Neuropsychology. https://doi.org/10.1111/jnp.70009
