ConceptM$^3$oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology

ConceptM³oE introduces a novel AI architecture that combines multimodal mixture-of-experts with interpretable concept bottlenecks for computational pathology, enabling medical AI models to provide transparent reasoning while maintaining competitive performance. The framework improves diagnostic accuracy in data-limited scenarios and demonstrates practical alignment with clinical decision-making processes.

ConceptM³oE represents a meaningful advancement in medical AI interpretability, addressing a critical gap between model performance and explainability in clinical settings. Traditional deep learning approaches in pathology achieve high accuracy but operate as black boxes, limiting adoption by clinicians who need to understand diagnostic reasoning. This architecture bridges that divide by embedding concept formation within mixture-of-experts pathways, allowing the model to decompose diagnostic evidence into distinct, human-understandable components while maintaining predictive power through residual pathways.

The technical innovation lies in the three-expert decomposition strategy—separating modality-specific, redundant, and synergistic evidence streams—which mirrors how pathologists integrate morphological, molecular, and textual diagnostic signals. By mapping latent features to structured concept hierarchies, the system generates verifiable reasoning traces validated by independent neuropathologists, establishing clinical credibility.

The performance improvements in data-limited regimes carry significant practical implications. Increasing macro-F1 from 56.41% to 66.70% at small training sizes addresses a real constraint in medical AI deployment, where large annotated datasets are expensive and rare. This suggests ConceptM³oE could accelerate adoption in specialized medical domains where data scarcity has historically hindered deep learning applications.

For the broader medical AI ecosystem, this work demonstrates that interpretability and performance are not inherent trade-offs but can be engineered through thoughtful architectural design. As regulatory bodies increasingly scrutinize AI decision-making in healthcare, interpretable-by-design approaches like this will likely become competitive advantages rather than nice-to-haves. The framework's demonstrated faster training convergence suggests efficiency gains that extend beyond accuracy metrics.

• →ConceptM³oE achieves competitive performance with traditional models while providing transparent, clinician-validated reasoning traces through structured concept bottlenecks.
• →The architecture improves performance in data-limited scenarios, increasing macro-F1 by 10.29 percentage points—addressing a critical constraint in medical AI deployment.
• →Mixture-of-experts decomposition into modality-specific, redundant, and synergistic pathways enables better integration of heterogeneous diagnostic inputs like images, text reports, and molecular measurements.
• →Residual pathways within experts prevent information loss from interpretable bottlenecks, maintaining the performance-interpretability balance necessary for clinical adoption.
• →The framework demonstrates faster training convergence, suggesting concept learning provides regularization benefits that reduce data requirements and computational overhead.