AI has already been associated with a range of negative findings, despite promising use-cases.

Early empirical research already shows neurobiological and psychosocial changes.

From "computational injury", improper AI use may lead to a syndromal clinical presentation in the near future.

A coherent research agenda is needed to study the impact of AI and guide best practices for AI use.

AI researchers (Tuckute et al., 2024) showed that a GPT-based encoding model could predict, and then deliberately drive or suppress, activation in human language networks —sentences the model rated as surprising and well-formed activated networks strongly, while bland, predictable text left them quiet. This study looked at suppression over a short timespan, but what happens from such suppression of language centers over time?

Like unused muscles, will brain areas atrophy and become permanently impaired? Negative neuroplastic effects—as contrasted with the beneficial impact of therapeutic agents on the brain, more familiar in discussions of neuroplasticity—could, if left unchecked, dominate over time and lead to irreversible impairment, particularly among those who overuse or misuse this powerful technology.

Take chronic traumatic encephalopathy (CTE) as a loose analogy—slow damage that accumulates in the brains of athletes from repeated head impacts, confirmable only at autopsy. Repeated injuries can add up to clinically significant damage—even smaller blows to the head once considered to be insignificant.

The story of CTE is a cautionary one. The carryover to AI is that we have chronic exposures already known to be toxic to a range of brain systems. Will we see "AI Brain" —a recognized AI-associated neuropsychiatric disorder, or AIAND—emerge as a clinical syndrome? What we don't know is what may happen over time. Selected potential mechanisms of injury, in this case "computational injury", are presented below, drawn from a growing body of research.

There are already findings on imaging suggestive of brain injury from AI. Geissler and colleagues (2023), using functional near-infrared spectroscopy, found reduced dorsolateral prefrontal cortex activation when participants offloaded tasks to a digital assistant—a candidate neural substrate for the disuse pattern. Zheng and colleagues (2025), using diffusion tensor MRI, found that microstructural integrity of frontal white-matter tracts predicts how people use external memory aids—evidence that cognitive-offloading behavior has a structural brain signature, though correlational rather than causal.

Skill Decay and Automation Bias

Dratsch and colleagues (2023) found that very experienced radiologists' diagnostic accuracy fell from........

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