Foundational values for foundation models

2026-08-10

AI Summary

This study investigates the normative values underlying researchers’ adoption or rejection of foundation models in medical imaging and their influence on technological decision-making. By integrating philosophical inquiry, literature analysis, and value-sensitive design, it systematically introduces research values into the ethical and philosophical discourse on foundation models for the first time. The work uncovers key value-laden factors that shape both technical choices and scholarly practices, elucidating the deeper motivations driving the deployment of foundation models in medical contexts. In doing so, it offers an original theoretical framework to guide the responsible development and application of artificial intelligence in healthcare.

Abstract

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.

Problem

• foundation models
• research values
• machine learning
• medical imaging
• normative dimension

Innovation

• foundation models
• research values
• normative dimension
• medical imaging
• Socratic approach