The AI inflection point in clinical neuropsychology: a call to action
Bibliographic record
Abstract
This commentary explores the rapidly evolving role of Artificial Intelligence (AI) in clinical neuropsychology, offering a critical framework for its responsible integration. Drawing on recent work that used AI to automate a neuropsychological screening tool; the article discusses key fears about AI and its more tangible risks, across safety, privacy, diagnostic bias, "erosion" of clinical judgment, and a lack of transparency. Rather than a disruptive or displacing force, this commentary argues that it represents a natural evolution of the historical shared commitment within neuropsychology and AI research to understand learning and adaptation. Key ideas are explored that highlight the value of AI as a powerful augmentative tool that automates discrete tasks, freeing neuropsychologists to focus on higher-level clinical and ethical duties. It concludes that whilst AI will not replace neuropsychologists, it is already permanently reshaping clinical workflows, decision-making and the broader contours of practice, as other key technological advances have historically achieved. Therefore, cultivating AI literacy is a fundamental step in effectively responding as opposed to reacting to these global changes. It dually challenges and positions our professional community to actively define sound ethical parameters, uphold scientific rigor, and ultimately leverage automation to enhance equitable access to high quality care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.042 | 0.056 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".