Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Background: This study aimed to assess the performance of ChatGPT, a large language model (LLM), on the Italian State Exam for Medical Residency (SSM) test to determine its potential as a tool for medical education and clinical decision-making support.
Materials And Methods: A total of 136 questions were obtained from the official SSM test. ChatGPT responses were analyzed and compared to the performance of medical doctors who took the test in 2022. Questions were classified into clinical cases (CC) and notional questions (NQ).
Results: ChatGPT achieved an overall accuracy of 90.44%, with higher performance on clinical cases (92.45%) than on notional questions (89.15%). Compared to medical doctors' scores, ChatGPT performance was higher than 99.6% of the participants.
Conclusions: These results suggest that ChatGPT holds promise as a valuable tool in clinical decision-making, particularly in the context of clinical reasoning. Further research is needed to explore the potential applications and implementation of large language models (LLMs) in medical education and medical practice.
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http://dx.doi.org/10.4415/ANN_23_04_05 | DOI Listing |