在皮肤颜料性皮肤病变的分类中,以皮肤学为重点的多模式大语言模型的验证
Joshua Mijares1, Neil Jairath1,2, Andrew Zhang1
1Department of Dermatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Diagnostics (Basel, Switzerland)
|November 13, 2025
概括
一个新的人工智能 (AI) 模型DermFlow在评估皮肤病变恶性瘤风险方面显示出高准确性,其表现优于其他人工智能和临床医生. 这种人工智能工具可以帮助皮肤科医生,特别是在专家稀缺的地方.
科学领域:
- 皮肤病学 皮肤病学
- 医疗人工智能 医疗人工智能
- 计算病理学计算病理学
背景情况:
- 人工智能 (AI) 越来越多地用于增强医疗诊断.
- 生成型人工智能模型可以解释复杂的临床数据,包括图像和患者病史,用于疾病风险评估.
- 多模式人工智能,集成各种数据类型,显示在专业医疗领域的承诺.
研究的目的:
- 评估皮肤学训练有素的多式大型语言模型DermFlow的诊断性能,以确定色素皮肤病变的恶性瘤风险.
- 将DermFlow的准确性与一般的大型语言模型 (Claude Sonnet 4) 和人类临床医生的准确性进行比较.
主要方法:
- 从59名患者的68个经过活检证明的有色素皮肤病变进行了回顾性分析.
- 在 DermFlow.com 中输入未被识别的患者病史和临床图像.
- 只有在克劳德·索内特4中输入临床图像来进行差异诊断.
- 将AI和临床医生诊断与组织病理学结果进行比较 (黄金标准).
主要成果:
- DermFlow实现了47.1%的最高诊断准确率和92.6%的任何诊断准确率,均衡准确率为91.7%.
- 在几个指标上,DermFlow与Claude (平衡精度67.1%) 和临床医生 (平衡精度75.8%) 相比表现优越.
- 在95.6%的病例中,DermFlow建议进行活检,高于Claude (82.4%).
结论:
- 对于有色素的皮肤病变,DermFlow的诊断性能与临床医生相比或优于临床医生,并且比Claude的诊断性能要好得多.
- 这些发现表明,皮肤病学中专门的AI模型具有潜在的临床实用性.
- 需要在现实环境中进一步验证,但人工智能可以在皮肤科医生访问有限的领域提供帮助.
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