从描述到诊断:评估AI在法医枪伤分类方面的能力
Francesco Sessa1, Elisa Guardo1, Massimiliano Esposito2
1Department of Medical, Surgical and Advanced Technologies "G.F. Ingrassia", University of Catania, 95121 Catania, Italy.
Diagnostics (Basel, Switzerland)
|August 28, 2025
概括
这项研究表明人工智能可以提高枪伤入口的识别能力, 人工智能显示出作为补充法医工具的潜力,
科学领域:
- 法医科学
- 医学成像
- 人工智能
背景情况:
- 人工智能在法医科学中的整合正在增长, 但在诊断枪支伤害方面的应用是有限的.
- 这项研究评估了ChatGPT-4能够区分枪伤入口和出口伤口的能力.
研究的目的:
- 评估ChatGPT-4对枪支伤害的诊断能力.
- 确定AI在法医病理学的潜力和局限性.
- 评估AI在区分入口和出口伤口中的应用性.
主要方法:
- ChatGPT-4在三个数据集上进行了测试:外部枪支伤害图像,完整的皮肤图像和真实枪支伤害图像.
- 在机器学习 (ML) 培训之前和之后评估了人工智能的性能.
- 使用描述性和推断性统计数据评估了分类准确性.
主要成果:
- 在ML训练后,ChatGPT-4显著改善了入口伤口的识别,增强了形态描述.
- 在分类出口伤口方面,人工智能的表现仍然有限,与法医文献的挑战一致.
- 人工智能在区分完整的皮肤和受伤时达到95%的准确性,但缺乏错误分类的上下文数据.
结论:
- 由于其学习能力,ChatGPT-4显示出作为补充法医诊断工具的潜力.
- 专门的深度学习模型目前在法医应用中优于ChatGPT-4.
- 专家监督对于减轻人工智能使用中的错误分类和幻觉风险至关重要.
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