Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework

Interpretability is essential for clinical adoption: The paper emphasizes that AI systems in healthcare must produce not only accurate results but also interpretable and understandable outputs to clinicians. Lack of interpretability creates mistrust and reluctance among healthcare professionals to adopt these technologies, making explainability a critical factor in responsible AI implementation.

Interpretability requires a multi-stage approach: The research identifies three key stages where interpretability must be built in—data pre-processing, model selection, and post-processing. This systematic breakdown suggests that transparency cannot be added as an afterthought but must be integrated throughout the entire AI development process in healthcare.

Multiple AI applications in healthcare demand different solutions: The paper reviews interpretability challenges across diverse medical AI tools including wearables, telemedicine systems, large language models, and digital twins. This suggests that a one-size-fits-all approach to responsible AI won't work; different healthcare applications require tailored interpretability strategies.

Quality control and communication are foundational: The research emphasizes the importance of "robust quality control to facilitate responsible communication between AI systems and clinicians." This highlights that responsible AI in healthcare isn't just about the technology itself, but about establishing clear, trustworthy communication channels between AI tools and the medical professionals who use them.

Implementation challenges remain significant: While the paper reviews existing interpretable AI methods and processes, it also identifies substantial challenges in implementing these approaches in real healthcare settings, suggesting that the gap between theoretical interpretability solutions and practical clinical deployment remains a critical area needing further research.