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Can an Automated Analysis of Radiology Reports Really Work?
Automated analysis of radiology reports is as accurate as manual auditing, but less expensive overall.
An automated analysis using natural language processing and machine learning algorithms can accurately define if reports are compliant with the use of standardized report templates and language, according to a study published in the Journal of the American College of Radiology.
Researchers from Texas sought to evaluate whether a software program using natural language processing and machine learning could accurately audit radiologist compliance with the use of standardized reports compared with manual audits.
The researchers reviewed radiology reports from a one-month period through their software program. Twenty-five reports for each of the 42 faculty members were also manually audited.
The results showed the manual audit provided a mean compliance rate of 91.2 percent compared with 92 percent for the automatic auditing program. The researchers concluded that the automatic audit was as accurate as manual audits, but with reduced labor costs.
New Study Examines Short-Term Consistency of Large Language Models in Radiology
November 22nd 2024While GPT-4 demonstrated higher overall accuracy than other large language models in answering ACR Diagnostic in Training Exam multiple-choice questions, researchers noted an eight percent decrease in GPT-4’s accuracy rate from the first month to the third month of the study.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
