计算机科学
极限(数学)
牙科
结果(博弈论)
数据挖掘
数据科学
风险评估
梅德林
医学
医学物理学
情报检索
人工智能
风险分析(工程)
作者
Vito Carlo Alberto Caponio,Alejandro I. Lorenzo Pouso,Marco Magalhaes,Aiman Ali,Daniela Adamo,Nicola Cirillo,Rosa María López-Pintor,Gennaro Musella
标识
DOI:10.1016/j.jdent.2025.106245
摘要
This study demonstrates that LLMs can achieve high accuracy in extracting single numeric outcomes, but omission errors in full-text analyses limit their independent use in SRMAs. Improving outcome reporting standards and leveraging accurate, lower-cost models may enhance evidence synthesis efficiency in dentistry and beyond.
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