A Survey on Trustworthiness in Foundation Models for Medical Image Analysis

The rapid advancement of foundation models in medical imaging represents a significant leap toward enhancing diagnostic accuracy and personalized treatment. However, the deployment of foundation models in healthcare necessitates a rigorous examination of their trustworthiness, encompassing privacy, robustness, reliability, explainability, and fairness. The current body of survey literature on foundation models in medical imaging reveals considerable gaps, particularly in the area of trustworthiness. Additionally, extant surveys on the trustworthiness of foundation models fail to address their specific variations and applications within the medical imaging domain. This survey paper reviews the current research on foundation models in the major medical imaging applications, with a focus on segmentation, medical report generation, medical question and answering (Q&A), and disease diagnosis, which includes trustworthiness discussion in their manuscripts. We explore the complex challenges of making foundation models for medical image analysis trustworthy, associated with each application, and summarize the current concerns and strategies to enhance trustworthiness. Furthermore, we explore the future promises of these models in revolutionizing patient care. Our analysis underscores the imperative for advancing towards trustworthy AI in medical image analysis, advocating for a balanced approach that fosters innovation while ensuring ethical and equitable healthcare delivery.