Influence of medical educational background on the diagnostic quality of ChatGPT‐4 responses in internal medicine: A pilot study
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| Title: | Influence of medical educational background on the diagnostic quality of ChatGPT‐4 responses in internal medicine: A pilot study |
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| Authors: | Gilardi, Nicolò, Ballabio, Massimo, Ravera, Francesco, Ferrando, Lorenzo, Stabile, Mario, Bellodi, Andrea, Talerico, Giovanni, Cigolini, Benedetta, Genova, Carlo, Carbone, Federico, Montecucco, Fabrizio, Bracco, Christian, Ballestrero, Alberto, Zoppoli, Gabriele |
| Source: | European Journal of Clinical Investigation. |
| Publisher Information: | Wiley, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | ChatGPT‐4, artificial intelligence, clinical decision making, diagnostic ranking, internal medicine, large language models |
| Description: | 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. |
| Document Type: | Article |
| Language: | English |
| ISSN: | 1365-2362 0014-2972 |
| DOI: | 10.1111/eci.70113 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....35896daab55d5c043f5ef4245c1c56b6 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | 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. |
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| ISSN: | 13652362 00142972 |
| DOI: | 10.1111/eci.70113 |