试论生成式人工智能的医疗应用能力与风险边界
Exploring the Capabilities and Risk Delineation of Generative AI in Medical Applications
王硕 1刘天语 2汪琛 3刘瑶瑶3
作者信息
- 1. 清华大学社会科学学院 北京 100084;东京大学卡维里宇宙物理学与数学研究所 日本千叶 277-8583
- 2. 清华大学人文学院 北京 100084
- 3. 清华大学社会科学学院 北京 100084
- 折叠
摘要
生成式人工智能医疗应用的伦理探讨需要对两个基本问题进行谨慎、清晰的划界.一方面,"能力"的划界旨在澄清生成式人工智能医疗应用的核心优势与技术局限,基于属种差异,明确生成式人工智能医疗应用相较于传统医疗人工智能的关键差异,避免陷入"ChatGPT全能论"或"ChatGPT无能论"的窠臼.另一方面,"风险"的划界旨在区分生成式人工智能医疗应用带来的问题类别及特征,厘清不同风险的独特性质与根源,包括传统人工智能的共有风险、生成式人工智能的特有风险以及生成式人工智能同医疗实践相结合的特性风险,推动风险的分类管理并推进伦理治理的不断完善.
Abstract
Ethical discussions on the application of generative artificial intelligence(AI)in healthcare require careful and clear delineation of two fundamental issues.On one hand,the delineation of"capabilities"aims to clarify the core advantages and technical limitations of generative AI in medical applications.Based on species differences,it is essential to delineate the key differences between generative AI medical applications and traditional medical AI,avoiding falling into the trap of"ChatGPT omnipotence"or"ChatGPT impotence".On the other hand,the delineation of"risks"aims to distinguish the categories and characteristics of problems brought about by generative AI medical applications,clarifying the unique nature and roots of different risks.This includes common risks of traditional AI,unique risks of generative AI,and characteristic risks associated with the integration of generative AI with medical practice.This promotes the classification and management of risks and advances the continuous improvement of ethical governance.
关键词
生成式人工智能/医疗应用/ChatGPT/能力划界/风险划界Key words
generative artificial intelligence/medical application/ChatGPT/capability delineation/risk delineation引用本文复制引用
基金项目
中国博士后科学基金面上项目(第七十四批)(2023)(2023M741978)
国家资助博士后研究人员计划(2023)(GZC20231384)
出版年
2024
NETL
NSTL
万方数据