ArticleKorean journal of family medicine2026
Hallucination in artificial-intelligence-assisted diagnosis of arthritis: a case report.
Article in Korean journal of family medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) technologies are increasingly being integrated into clinical practice, offering potential enhancements in diagnostic accuracy and clinical efficiency. In this case report, a diagnostic attempt assisted by ChatGPT-4o in a 51-year-old female patient presenting with hand arthralgia is described. The AI-generated interpretation demonstrated hallucination-namely, the fabrication of unsupported or inaccurate information-in the analysis of radiologic and laboratory findings, as well as in treatment recommendations. This case underscores the importance of exercising caution when applying AI tools in clinical contexts. To ensure diagnostic accuracy, patient safety, and ethical responsibility, expert oversight and multi-step verification processes are essential in the deployment of AI-generated clinical outputs.
Indexed as
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.