对人工智能作为法医毒理学分类工具的初步评估案例解释
Efrosyni Riga1, Hilary J Hamnett1, L Nitin Seetohul1
1Department of Chemistry, University of Lincoln, Lincoln, United Kingdom.
Forensic science international
|March 15, 2026
概括
大型语言模型 (LLM) 在法医毒理学解释方面表现有前途,但需要监督. 虽然在识别物质方面准确,但LLM表现出过度自信和缺乏细微差别,限制了它们目前在证据上下文中的使用.
科学领域:
- 法医毒理学 法医毒理学
- 计算毒理学计算毒理学
- 医学中的人工智能
背景情况:
- 大型语言模型 (LLM) 显示了科学解释的潜力.
- 法医毒理学的LLM应用尚未被探索.
- 专家对体液中药物度的解释对于法律和医疗方面至关重要.
研究的目的:
- 评估LLM在解释法医毒理学病例数据方面的表现.
- 为了比较LLM解释准确性和特征与人类专家.
- 评估LLM作为法医毒理学中潜在的分类工具.
主要方法:
- 从已发表的文献中使用了十个匿名的法医毒理学案例.
- 案例数据作为提示向微软365 CoPilot和ChatGPT (3.5 版本) 提交.
- 人工智能输出与专家解释的准确性,风险分类,警告和信心表达相比较.
主要成果:
- 在各个案例中,LLM正确识别了15种物质中的93%.
- 在70%的案例中,LLM使用过度自信的语言.
- 在LLM与专家提供的警告的数量和细微差别中发现了显著的差异.
结论:
- 在专家监督下,LLM可以协助初步的法医毒理学分类.
- 由于识别错误,不适当的语言和不一致的解释,目前在证据设置中不支持LLM的使用.
- 需要进一步开发来解决LLM在法医毒理学微妙和警告应用方面的局限性.
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