Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications

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....

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Veröffentlicht in:IEEE transactions on medical imaging Jg. 44; H. 10; S. 4098 - 4115
Hauptverfasser: Fei Du, Rui, Lloret Carbonell, Eduard, Huang, Jiaxuan, Liu, Sheng, Wang, Xiaohang, Shen, Dinggang, Ke, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2025
Schlagworte:
Accuracy
Adaptation models
AI trustworthiness
Artificial intelligence
Artificial Intelligence - ethics
Computational modeling
computational pathology
Computer architecture
Ethics
Foundation model
Foundation models
Humans
Medical diagnostic imaging
Pathology
Pathology - ethics
Training
ISSN:0278-0062, 1558-254X, 1558-254X
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2025.3551913