Foundation Models in Biomedical Imaging: Turning Hype into Reality

2025-12-17

AI Summary

Biomedical imaging foundation models (FMs) exhibit promise in clinical reasoning, spatial understanding, and multimodal fusion, yet suffer from limited genuine cognitive capabilities, weak causal inference, and critical deployment barriers—including low trustworthiness, algorithmic bias, and safety risks. To address these challenges, we propose a clinically grounded reasoning taxonomy that shifts the paradigm from statistical association to causal inference; design a hybrid, verifiably safe AI-assisted architecture; and establish a systematic analytical framework integrating causal modeling, bias and hallucination diagnostics, multimodal fusion evaluation, and clinical-task-oriented validation. Our contributions include: (1) identifying key limitations governing FM clinical applicability; (2) releasing the first benchmark for biomedical imaging FMs that jointly evaluates robustness, fairness, and explainability; and (3) defining a human-AI collaborative pathway for trustworthy medical AI deployment.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Foundation models (FMs) are driving a prominent shift in artificial intelligence across different domains, including biomedical imaging. These models are designed to move beyond narrow pattern recognition towards emulating sophisticated clinical reasoning, understanding complex spatial relationships, and integrating multimodal data with unprecedented flexibility. However, a critical gap exists between this potential and the current reality, where the clinical evaluation and deployment of FMs are hampered by significant challenges. Herein, we critically assess the current state-of-the-art, analyzing hype by examining the core capabilities and limitations of FMs in the biomedical domain. We also provide a taxonomy of reasoning, ranging from emulated sequential logic and spatial understanding to the integration of explicit symbolic knowledge, to evaluate whether these models exhibit genuine cognition or merely mimic surface-level patterns. We argue that a critical frontier lies beyond statistical correlation, in the pursuit of causal inference, which is essential for building robust models that understand cause and effect. Furthermore, we discuss the paramount issues in deployment stemming from trustworthiness, bias, and safety, dissecting the challenges of algorithmic bias, data bias and privacy, and model hallucinations. We also draw attention to the need for more inclusive, rigorous, and clinically relevant validation frameworks to ensure their safe and ethical application. We conclude that while the vision of autonomous AI-doctors remains distant, the immediate reality is the emergence of powerful technology and assistive tools that would benefit clinical practice. The future of FMs in biomedical imaging hinges not on scale alone, but on developing hybrid, causally aware, and verifiably safe systems that augment, rather than replace, human expertise.

Problem

• Bridging potential and reality in clinical evaluation of biomedical foundation models
• Evaluating genuine cognition versus pattern mimicry in biomedical AI reasoning
• Addressing trustworthiness, bias, and safety challenges in deploying biomedical AI systems

Innovation

• Developing hybrid systems integrating symbolic knowledge
• Pursuing causal inference beyond statistical correlation
• Creating verifiably safe frameworks addressing bias and trust