AI 中文总结
本文探讨医学影像机器学习领域的基础模型，通过苏格拉底式研究价值分析，明确使用或弃权基础模型的依据，阐明其与医学机器学习哲学的契合方式。
AI 中文摘要
研究价值是具有独特规范维度的属性，常通过影响技术决策方式，直接或间接影响技术研究的开展。在医学影像机器学习领域，理解这些价值有助于明确研究者为其发表成果中的决策提供的依据，以及解释某些技术为何在科学文献和临床中普及（或不普及）。本文探讨其中一项技术——基础模型，为使用及弃权（不执行）基础模型的行为找到了详细依据。通过对这一特定技术决策产生的研究价值采取苏格拉底式方法，本文旨在更好地阐明基础模型如何契合医学机器学习哲学。
英文摘要
Research values, properties with a distinctive normative dimension, often affect how technological research is performed in both direct and indirect ways by influencing how technical decisions are made. In machine learning for medical imaging, understanding these values can be important for understanding why particular researchers justify the decisions made in their publications and explain why certain technologies become ubiquitous (or not) in the scientific literature and in the clinic. This article explores one of these technologies, foundation models, finding detailed justifications both for their use and abstention from their use. By taking a Socratic approach to research values arising from this specific technical decision, this article aims to better illustrate how foundation models fit into the philosophy of machine learning in medicine.
Comments12 pages, 1 figure, accepted to the MICCAI 2026 Workshop on Fairness, Regulation, and Ethics (FAIMI-BRIDGE-EPIMI 2026)
