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: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
In recent years, radiologic imaging has undergone tremendous technological advances and is now a pillar of diagnostic and treatment algorithms in clinical medicine. The increased complexity and volume of medical imaging has led clinicians to become ever more reliant on radiologists to both identify and interpret patient studies. A radiologist's report provides key insights into a patient's immediate state of health, information that is vital when choosing the most appropriate next steps in management. As errors in imaging interpretation or miscommunication of results can greatly impair patient care, identifying common error sources is vital to minimizing their occurrence. Although mistakes in medical imaging are practically inevitable, changes to the delivery of imaging reporting and the addition of artificial intelligence algorithms to analyze clinicians' communication skills can minimize the impact of these errors, keep up with the continuously evolving landscape of medical imaging, and ultimately close the communication gap.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323531 | PMC |
http://dx.doi.org/10.3390/diagnostics12071761 | DOI Listing |
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