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Sökning: onr:"swepub:oai:gup.ub.gu.se/334159" > Evaluating the Effi...

Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine

Guillen-Grima, Francisco (författare)
Guillen-Aguinaga, Sara (författare)
Guillen-Aguinaga, Laura (författare)
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Alas-Brun, Rosa (författare)
Onambele, Luc (författare)
Ortega, Wilfrido (författare)
Montejo, Rocio (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för obstetrik och gynekologi,Institute of Clinical Sciences, Department of Obstetrics and Gynecology
Aguinaga-Ontoso, Enrique (författare)
Barach, Paul (författare)
Aguinaga-Ontoso, Ines (författare)
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 (creator_code:org_t)
2023
2023
Engelska.
Ingår i: CLINICS AND PRACTICE. - 2039-7275 .- 2039-7283. ; 13:6, s. 1460-1487
Relaterad länk:
https://gup.ub.gu.se...
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https://doi.org/10.3...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. Material and methods: We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability. Results: GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)

Nyckelord

machine learning
artificial intelligence
ChatGPT
GPT-3.5
GPT-4
medical education
quality of care
patient safety
image
large language model

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