AI Resilience Report forNeuropsychologists

Right now, AI is mostly augmenting neuropsychologists rather than replacing them. The biggest growth area is paperwork. Health systems and mental healthcare providers are increasingly adopting AI tools to support care delivery, though concerns about safety, job replacement and clinical oversight remain.

Some providers have begun using AI for administrative tasks such as documentation, billing and updating EHRs, with nearly 40 products offering transcription and documentation support, according to Becker's Behavioral Health ^[1].

For clinical work specifically, the American Academy of Clinical Neuropsychology ^[2] notes that AI can unlock hidden information in big data, reduce diagnostic and therapeutic errors, and make real-time inferences for health risks and outcome prediction. Efficient analysis of data could allow triaging where AI software ranks patients in order or priority, and that AI use in scoring could save clinicians time better spent helping patients directly (e.g., automated scoring of thousands of RCFT and clock drawings). A new commentary from the American Board of Professional Psychology ^[3] describes prototype systems where an ambient audio monitoring system captures verbal responses from examinees in real time, scores them according to standardized instructions and normative data, automatically generates score reports, and stores item-level responses.

Specialized report-writing tools and large language models are also being studied in The Clinical Neuropsychologist ^[4] for scoring assistance and drafting.

Adoption pressure is real because demand far exceeds supply: ABPP notes with fewer than 6,000 clinical neuropsychologists practicing in the U.S., demand for neuropsychological evaluation far outpaces supply, and traditional approaches compound access limitations with labor-intensive workflows involving several hours of standardized test administration, hand scoring, and preparation of lengthy evaluation reports. A 2026 Wiley review in the Journal of Neuropsychology ^[5] similarly highlights AI's growing role in cognitive assessment of neurodegenerative disorders.

But adoption is slowing for several good reasons. NPR reported in April 2026 ^[6] that John Torous, MD, director of digital psychiatry at Beth Israel Deaconess Medical Center in Boston, said clinical use of AI remains limited because tools are not well tested and require significant infrastructure and cost to implement, and many smaller practices and community mental health centers lack the IT resources needed to deploy these systems. Workforce pushback is also growing, with about 2,400 Kaiser Permanente mental health providers holding a 24-hour strike, citing changes to triage workflows and reduced use of licensed clinicians in triage.

Ethics, privacy, test security, and bias remain unresolved — the AACN warns that there are no standards to assess the safety and efficacy of AI, patient privacy and safety concerns exist due to risks with cloud-enabled data storage, and behavioral observations are yet to be solidified into AI; a highly anxious patient with an amnestic memory profile might not be classified accurately.

The good news for anyone considering this field: the parts of the job that depend on human judgment — interviewing patients, weighing surgical risks, integrating messy life context, and supporting families through brain injury or dementia — are exactly what AI handles worst. Experts expect a hybrid model in which clinicians work alongside AI tools to support patient care, including therapy support, skills practice and real-time patient feedback. In other words, future neuropsychologists will likely spend less time scoring tests and writing boilerplate, and more time doing the deeply human work that drew people to the field in the first place.