Medical image analysis has rapidly advanced in the identification and characterization of lesions. Despite advancements, deep learning models for medical image analysis often face the dual challenges of "black-box decision-making" and "dependence on statistical associations." Traditional methods base the fitting of imaging features with pathological outcomes on large datasets but fail to account for causal mechanisms from confounding factors, leading to higher misdiagnosis rates under complex clinical conditions. More critically, as these models lack causal reasoning capabilities, they also fall short in mapping interpretable pathways that align with clinical reasoning. It is crucial for physicians who need evidence of causal sequences beyond feature heatmaps. Consequently, a novel diagnostic paradigm driven by clinical causal cognition emerges as the breakthrough direction. A hybrid intelligent system, grounded on pathophysiological insights and enhanced by human-machine interaction alongside clinical and multiomics data, facilitates the transformation of imaging features into comprehensible and traceable causal frameworks.

This Research Topic aims to address the challenges of "black-box decision-making" and "dependence on statistical associations” inherent in current medical image analysis models. By developing a hybrid intelligent system integrating pathophysiological knowledge with active human-machine interaction, it is possible to transpose imaging features into traceable causal networks. This system ensures that AI decision-making logics align with clinical cognition, thus improving the reliability and accuracy of AI-assisted diagnostics. Ultimately, the goal is to establish a diagnostic paradigm where AI reasoning matches physicians' clinical thinking, ensuring more accurate and dependable medical diagnoses.

To gather further insights to enhance AI interpretability in medical diagnostics, we welcome articles addressing, but not limited to, the following themes:
o Interpretable AI in reducing the misdiagnosis rate of medical imaging
o Strategies for improving AI decision logic based on clinical cognition
o Integrating multimodal data (imaging, clinical, and multiomics) for interpretable and context-aware AI diagnostics
o Pathophysiological knowledge-driven approaches for interpretable lesion recognition
o Human-computer collaboration frameworks for the identification and characterization of lesions
o Graph network-based methods for interpretable diagnostic insights

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From Black-box to Clarity in Lesion Diagnostics: Clinical Causal Cognition Led Interpretable Diagnostic AI Systems

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