Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Influence of medical educational background on the diagnostic quality of <scp>ChatGPT</scp>‐4 responses in internal medicine: A pilot study
2
Zitationen
14
Autoren
2025
Jahr
Abstract
This pilot study evaluated the influence of medical background on the diagnostic quality of ChatGPT-4's responses in Internal Medicine. Third-year students, residents and specialists summarised five complex NEJM clinical cases before querying ChatGPT-4. Diagnostic ranking, assessed by independent experts, revealed that residents significantly outperformed students (OR 2.33, p = .007); though overall performance was low. These findings indicate that user expertise and concise case summaries are critical for optimising AI diagnostics, highlighting the need for enhanced AI training and user interaction strategies.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.287 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.140 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.534 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.450 Zit.