ArticleJMIR medical informatics2024
Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation.
Article in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Correction: Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation.JMIR medical informatics · 2026Article
- Generative artificial intelligence as an aid in interpreting thyroid FNA cytopathological images: Are we there yet?Cancer cytopathology · 2026Article
- Performance evaluation of large language models in pediatric nephrology clinical decision support: a comprehensive assessment.Pediatric nephrology (Berlin, Germany) · 2025Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has shown great promise in assisting medical diagnosis, but its application in renal pathology remains limited.
objectiveWe evaluated the performance of an advanced AI language model, Claude 3 Opus (Anthropic), in generating diagnostic descriptions for renal pathological images.
methodsWe carefully curated a dataset of 100 representative renal pathological images from the Diagnostic Atlas of Renal Pathology (3rd edition). The image selection aimed to cover a wide spectrum of common renal diseases, ensuring a balanced and comprehensive dataset. Claude 3 Opus generated diagnostic descriptions for each image, which were scored by 2 pathologists on clinical relevance, accuracy, fluency, completeness, and overall value.
resultsClaude 3 Opus achieved a high mean score in language fluency (3.86) but lower scores in clinical relevance (1.75), accuracy (1.55), completeness (2.01), and overall value (1.75). Performance varied across disease types. Interrater agreement was substantial for relevance (κ=0.627) and overall value (κ=0.589) and moderate for accuracy (κ=0.485) and completeness (κ=0.458).
conclusionsClaude 3 Opus shows potential in generating fluent renal pathology descriptions but needs improvement in accuracy and clinical value. The AI's performance varied across disease types. Addressing the limitations of single-source data and incorporating comparative analyses with other AI approaches are essential steps for future research. Further optimization and validation are needed for clinical applications.
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