Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models

TL;DR

This paper introduces a systematic framework for quantifying ethical risks in pathology foundation models, addressing privacy, fairness, and reliability concerns crucial for safe clinical deployment.

Contribution

It pioneers the first quantitative evaluation framework for ethical risks in PFMs, including privacy leakage, performance disparities, and reliance on irrelevant features.

Findings

Identified significant privacy leakage risks in PFMs.

Detected performance disparities across demographic groups.

Highlighted the influence of irrelevant features on model decisions.

Abstract

Pathology foundation models (PFMs), as large-scale pre-trained models tailored for computational pathology, have significantly advanced a wide range of applications. Their ability to leverage prior knowledge from massive datasets has streamlined the development of intelligent pathology models. However, we identify several critical and interrelated ethical risks that remain underexplored, yet must be addressed to enable the safe translation of PFMs from lab to clinic. These include the potential leakage of patient-sensitive attributes, disparities in model performance across demographic and institutional subgroups, and the reliance on diagnosis-irrelevant features that undermine clinical reliability. In this study, we pioneer the quantitative analysis for ethical risks in PFMs, including privacy leakage, clinical reliability, and group fairness. Specifically, we propose an evaluation framework that systematically measures key dimensions of ethical concern: the degree to which patient-sensitive attributes can be inferred from model representations, the extent of performance disparities across demographic and institutional subgroups, and the influence of diagnostically irrelevant features on model decisions. We further investigate the underlying causes of these ethical risks in PFMs and empirically validate our findings. Then we offer insights into potential directions for mitigating such risks, aiming to inform the development of more ethically robust PFMs. This work provides the first quantitative and systematic evaluation of ethical risks in PFMs. Our findings highlight the urgent need for ethical safeguards in PFMs and offer actionable insights for building more trustworthy and clinically robust PFMs. To facilitate future research and deployment, we will release the assessment framework as an online toolkit to support the development, auditing, and deployment of ethically robust PFMs.