皮肤病学"AI巴比伦":人工智能制造的皮肤病学描述的跨语言评估
Emmanouil Karampinis1,2, Christina-Marina Zoumpourli3, Christina Kontogianni4
1Second Dermatology Department, School of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
Medicina (Kaunas, Lithuania)
|January 28, 2026
概括
该研究发现,提示中使用的语言显著影响AI生成的皮肤病描述. 英语提示产生了更准确和可读的输出,而非英语提示和皮肤镜像在术语一致性方面提出了挑战.
科学领域:
- 医疗信息学 医疗信息学
- 皮肤病学 皮肤病学
- 自然语言处理自然语言处理.
背景情况:
- 皮肤病学使用复杂的术语来描述病变,皮肤镜检查增加了进一步的复杂性和主观性.
- 人工智能驱动的工具为标准化皮肤学描述提供了潜在的解决方案.
研究的目的:
- 评估聊天机器人 (Gemini 2) 在生成多语言皮肤病描述方面的能力.
- 评估即时语言对人工智能生成的皮肤病学报告的可读性,完整性和术语一致性的影响.
主要方法:
- 用了皮肤病变的五张宏观和五张皮肤镜像,并使用了特定语言的提示.
- 对生成的描述进行了可读性 (LIX评分) 和质量 (CLEAR评估) 的分析.
- 用英语,法语,德语和希腊语输出的SNOMED CT映射来评估术语一致性.
主要成果:
- 英语和法语的描述更复杂,但读起来更难.
- 英语文本和宏观图像产生了最高的准确性,完整性和可读性.
- 德国的输出显示了最大的术语不匹配,而法国的术语不匹配最小.
结论:
- 迅速的语言极大地影响了人工智能生成的皮肤病学描述.
- 跨语言的变化和术语不一致凸显了需要仔细及时的工程.
- 皮肤镜像和非英语提示对当前的人工智能模型构成更大的挑战.
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