The rapid advancement of foundation models in medical imaging is a promising development that has the potential to greatly enhance diagnostic accuracy and personalized treatment in healthcare. However, incorporating these models into medical practice requires careful consideration of their trustworthiness. Trustworthiness encompasses various aspects including privacy, robustness, reliability, explainability, and fairness. In order to fully assess the trustworthiness of foundation models, it is important to conduct thorough examinations and evaluations.
While there is a growing body of literature on foundation models in medical imaging, there are significant gaps in knowledge, particularly in the area of trustworthiness. Existing surveys on trustworthiness tend to overlook the specific variations and applications of foundation models within the medical imaging domain. This survey paper aims to address these gaps by reviewing current research on foundation models in major medical imaging applications such as segmentation, medical report generation, medical question and answering (Q&A), and disease diagnosis. The focus of these reviews is on papers that explicitly discuss trustworthiness.
It is important to explore the challenges associated with making foundation models trustworthy in each specific application. For example, in segmentation tasks, trustworthiness can be compromised if the model fails to accurately identify and classify the different regions of an image. Similarly, in medical report generation, errors or biases in the model’s predictions can undermine trust. Ensuring trustworthiness in medical Q&A and disease diagnosis is also crucial, as incorrect or unreliable answers can have serious consequences for patient care.
The authors of this survey paper summarize the current concerns and strategies for enhancing trustworthiness in foundation models for medical image analysis. They also highlight the future promises of these models in revolutionizing patient care. It is clear that trustworthiness is a critical factor in the successful deployment of these models in healthcare, and there is a need for a balanced approach that fosters innovation while maintaining ethical and equitable healthcare delivery. Advances in trustworthiness evaluation methods, transparency in model development, and standardized guidelines can all contribute to achieving trustworthy AI in medical image analysis.
Key Takeaways:
- The deployment of foundation models in healthcare requires a rigorous examination of their trustworthiness.
- Existing surveys on foundation models in medical imaging lack focus on trustworthiness and fail to address specific variations and applications.
- This survey paper reviews research on foundation models in major medical imaging applications, emphasizing trustworthiness discussions.
- Challenges in making foundation models trustworthy vary across applications such as segmentation, medical report generation, Q&A, and disease diagnosis.
- The paper highlights current concerns, strategies, and future promises of foundation models in revolutionizing patient care.
- A balanced approach is necessary to foster innovation while ensuring ethical and equitable healthcare delivery.
In conclusion, the survey paper emphasizes the importance of trustworthiness in foundation models for medical imaging. Addressing the gaps in existing literature and exploring the challenges and strategies associated with trustworthiness will contribute to the advancement of trustworthy AI in healthcare. The potential benefits of these models in improving diagnostic accuracy and personalized treatment are substantial, but it is essential to prioritize the ethical and equitable delivery of healthcare in their development and deployment.