Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications - Fei Du, Rui; Lloret Carbonell, Eduard; Huang, Jiaxuan; Liu, Sheng; Wang, Xiaohang; Shen, Dinggang; Ke, Jing (IEEE transactions on medical imaging, 2025)
Artificial intelligence (AI) has profoundly transformed our lives, reshaping industries and impacting nearly every aspect of society over the past few decades. It has recently become even more influential, primarily due to the rise of foundation models representing a new paradigm in AI development. These models, characterized by their large-scale training on vast datasets, have unique capabilities such as emergence and transference, enabling them to generalize across diverse tasks. Since their introduction, foundation models have been increasingly applied in fields such as autonomous driving, computer vision, marketing, finance, industrial robotics, and healthcare. Pathologists worldwide use computational methods to analyze diseases that profoundly impact human well-being, including cancer diagnosis and staging, genetic mutation prediction, and treatment and prognosis forecasting. In this article, we discuss how, despite the promise of foundation models in various applications, their development and application in computational pathology remain challenging due to inherent characteristics such as emergence, homogenization, hallucination, transference, compositionality, and explainability. While powerful, these traits introduce numerous ethical concerns and challenges, impacting safety and reliability, patient privacy, accountability, and equity and fairness in healthcare access. We examine these ethical issues, focusing on key concerns like algorithmic discrimination and misuse, accuracy, privacy breaches, transparency, public accessibility, and accountability. Furthermore, potential solutions to these challenges are analyzed, offering future perspectives on promoting the development and application of more ethical AI and foundation models in computational pathology. These insights aim to guide foundation models toward responsible integration of AI in healthcare.