Can AI Replace Neuropsychological Tests?

By Vedanshi Raheja

Many celebrate upon the advent of AI, its sterling performance and how it provides assistance currently. However, others question what if AI outperforms humans. This article will be clearing the widespread belief that AI can supplant traditional  neuropsychological tests. Saying AI will replace these tests is like saying YouTube videos or textbooks will take over teachers and professors. While these tools are momentous and aim to provide greater understanding, they cannot replace the guidance, personal insight and experience of an actual teacher. Similarly, AI cannot excel or remove the diagnosis and supplant the insight from these. It is essential to clear out this common misconception about AI. The media often hypes the public to forestall AI’s use in healthcare because of its potential errors and sociotechnical imaginaries (over exaggerating using terms such as AI taking over all hospitals). Due to this, patients get biased and think that AI is not a reliable and trustable tool, which could miss out on a big opportunity for better treatment deliveries. AI does not do the work, it just assists and adds additional insights, making work more easier and impactful. Peer review studies unveil that though AI matches and surpasses in accuracy from traditional neuropsychological tests, the most it can do is be an augment rather than a replacement. AI is still under several ethical concerns such as bias, data privacy, accountability, workflow integration and the overall trust issue patients may feel if solely AI treats them. AI is being used as a tool to assist and facilitate work, not to replace traditional neuropsychological tests that have been and will continue to be trusted.

Early diagnosis and treatment for neurocognitive disorders are an arduous challenge in healthcare because they are degenerative, and there is a lack of effective treatments to diagnose and prevent worsening. Nevertheless, AI can breach boundaries by efficiently analyzing complex datasets, surpassing traditional methods,and improving early detection of cognitive diseases. It can identify major patterns that may not be observed by the human eye or through conventional statistics. The integration of AI can significantly revolutionize the detection and treatment of neurocognitive disorders. These ML (Machine Learning) models outperformed practicing neurologists and neuroradiologists in diagnosing individuals with Mild Cognitive Impairment or dementia. For example, the mobile screening test for MCI was developed due to the limited accuracy of the standard Montreal Cognitive Assessment. AI had better precision compared to original tests when being assessed for amnestic-MCI. Furthermore, ML models potentially play a potent role in screening for cognitive dysfunction. Through analyzing neuropsychological, neurophysiological, and clinical data, AI was able to predict progression towards MCI. By using mobile based tests and game based intelligence tests, ML can successfully evaluate neurocognitive disorders. Tools like Panoramix use virtual reality and neuropsychological assessment to detect early cognitive markers of Alzheimer’s Disease and MCI, achieving a notable 100% success rate in classifying people with cognitive impairments from those who are healthy. Furthermore, Panoramix 2.0 is now an environmentally friendly, non-intrusive and frustration free tool, also obtaining user acceptance. Integration of AI and ML creates pathways for innovative diagnostic tools and improved treatment plans, lowering the drudgery in healthcare while obtaining better patient results. Its accuracy is even greater than some traditional methods, aiming for better detection and recovery for patients. 

 However, it is important to consider ethics for AI examination. As the technology advances, it is crucial to maintain privacy and prevent any potential biases in data collection and predictions. Another major challenge that is posed by using AI is making doctors have to enter more data about patients. AI isn’t really reducing the burden from doctors, it should be able to interpret all the data by itself and evaluate instead of having a doctor to enter all the data and make them analyze.. Additionally, these data driven technologies lack emotional and contextual understanding. Clinicians need to balance the use of technology, as they may miss perspectives that human connection and context provide for neuropsychological assessments.  For the future, these technologies should focus more on facilitating the work of neurologists instead of just being accurate. Furthermore, human understanding is necessary in the realm of neuropsychological assessments which AI wouldn’t be able to achieve.

References

Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024, March 5). Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews. Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews. Retrieved May 31, 2025, from https://pmc.ncbi.nlm.nih.gov/articles/PMC10916499/

Murdoch, B. (2021, September 15). Privacy and artificial intelligence: challenges for protecting health information in new era-pmc. Privacy and artificial intelligence: challenges for protecting health information in a new era. Retrieved M

Veneziani, I., Marra, A., Formica, C., Grimaldi, A., Marino, S., Quartarone, A., & Maresca, G. (2024, January 19). Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review. Retrieved May 31, 2025, from https://pmc.ncbi.nlm.nih.gov/articles/PMC10820741/