Hallucination in artificial-intelligence-assisted diagnosis of arthritis: a case report

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.

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Publication Details

Journal
Korean Journal of Family Medicine
Published
2025-10-29
DOI
https://doi.org/10.4082/kjfm.25.0147
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65
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article

Hallucination in artificial-intelligence-assisted diagnosis of arthritis: a case report

Dae Hyun Kim
1 citations
Korean Journal of Family Medicine
Artificial Intelligence in Healthcare and Education
0.65
article

Hallucination in artificial-intelligence-assisted diagnosis of arthritis: a case report

Dae Hyun Kim
article en
1 citations

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.

Korean Journal of Family MedicineVol. 47(2)
Keimyung University (KR)
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare and Education
0.65
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