ReviewNPP - digital psychiatry and neuroscience2026
Does ChatGPT need a psychiatrist? Similarities between human psychopathology and errors in large language models.
Review in NPP - digital psychiatry and neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Generative AI in psychiatric education: balancing risks and benefits for responsible integration.Frontiers in psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Two striking phenomena of the human mind encountered in mental healthcare are hallucinations and confabulations; perceiving things that are not there, or filling memory gaps with invented stories. Interestingly, contemporary artificial intelligence systems, such as large language models (LLMs) and automatic speech recognition tools, show remarkably similar errors. They are known to "hallucinate" words, or "confabulate" facts when information is missing, producing output that feels coherent but is false. In this article, we explore these parallels between psychiatric symptoms in humans and mistakes in model output. By comparing how and why these errors arise, we aim to illuminate shared computational principles underlying predictive systems. These comparisons highlight both the risks of relying on imperfect AI systems and the opportunity to use them as computational mirrors to better understand the human mind and the other way around: knowledge from psychiatric symptoms may help to improve AI systems to reduce error rates.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.