Evidence map›Paper›PMID 42396382›Full record

ArticleEuropean heart journal. Digital health2026

Artificial stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.

Alan G Fraser

Abstract read
In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Alan G FraserUniversity Hospital of Wales, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XW, UK.ORCID https://orcid.org/0000-0001-7083-6995

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

'Artificial intelligence' (AI), as a blanket term, covers many advanced computing techniques that employ divergent methodologies for a wide array of functions. It is too late to affect general usage of the term without qualification-although it is a misnomer-but perhaps not too late to argue for the preferential use of more specific and realistic terminologies when discussing useful applications in science and medicine. Large language models are statistical tools for predicting text; machine learning algorithms are programs that discern patterns from data; and neural networks are mathematical models that predict outputs from inputs. In all cases, the software is unaware of what it is doing or why. The real intelligence is human, exemplified by the expertize of the engineers who designed any particular system, and by the scepticism, realism and vision of those who interpret and apply its outputs. Users may suspend their incredulity if large language models that are not sentient creatures are programmed to answer questions in the first person, since that encourages anthropomorphism. Perhaps we need a new word-which could be '

Indexed as

AnthropomorphismArtificial intelligenceHealthcareLogimorphismMachine learningRisks

Identifiers

PMID42396382
PMCPMC13326636

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.