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Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models—Bing, Claude, ChatGPT, and Perplexity
21
Zitationen
8
Autoren
2024
Jahr
Abstract
The study revealed variations in accuracy across LLMs for both OE and SATA questions. Perplexity showed superior performance in OE questions, while Bing excelled in SATA questions. OE queries yielded better overall results. The current inconsistencies in LLM accuracy highlight the importance of further refinement before these tools can be reliably integrated into clinical practice, with a need for additional LLM fine-tuning and judicious selection by radiologists to achieve consistent and reliable support for decision-making.
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