Deep Learning Diagnostic Systems in Healthcare: Addressing Black Box Transparency with Conformal Analysis for Skin Lesion Classification

In a study published on March 31, 2025, Swarnava Bhattacharyya and colleagues introduced Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics, applying conformal analysis to enhance the transparency of Vision Transformer models in diagnosing skin lesions across diverse populations.

Deep learning systems for medical image analysis match human expert performance but lack transparency, hindering adoption in healthcare. Recent large foundation models, trained on vast datasets, generalize well across tasks but remain opaque due to uninterpretable embeddings. To address this, conformal analysis quantifies predictive uncertainty of a vision transformer (ViT) model for skin lesion classification across patient demographics, including sex, age, and ethnicity. This approach enhances trust in clinical applications by providing interpretable uncertainty estimates.

In healthcare, artificial intelligence (AI) is revolutionizing diagnostics, particularly in dermatology. However, the adoption of AI models faces hurdles due to their opacity and potential biases. This article explores a novel approach using conformal analysis on vision transformer (ViT) models to enhance transparency and fairness in skin lesion classification.

The study introduces conformal analysis as a tool for uncertainty quantification in ViT models, addressing the black box issue inherent in deep learning. By integrating this method with dynamic F1-score-based sampling, researchers mitigate class imbalance, ensuring more equitable model performance across diverse patient demographics. This approach not only enhances accuracy but also provides interpretable results, crucial for clinical trust.

Transparency is paramount in healthcare AI, where decisions can significantly impact lives. Foundation models like ViTs, while powerful, pose challenges due to their complexity and size. Conformal analysis offers a solution by providing prediction confidence intervals, making the model’s reasoning more accessible. This method also tackles fairness issues, ensuring that predictive accuracy isn’t skewed towards majority groups, thus broadening applicability.

Algorithmic fairness in AI refers to the equitable performance of models across different demographics. In healthcare, this is vital for patient trust and ethical practice. The study demonstrates how conformal analysis ensures that Google’s DermFoundation model performs consistently across various sex, age, and ethnic groups, preventing bias and enhancing reliability.

This research presents a significant advancement in AI-driven diagnostics by combining conformal analysis with ViT models to achieve transparency and fairness. By addressing class imbalance and ensuring equitable performance, the study paves the way for more trustworthy clinical AI tools, ultimately improving patient outcomes through reliable and unbiased diagnosis.

Integrating conformal analysis into skin lesion classification marks a step forward in making AI tools more transparent and fair. As healthcare continues to embrace AI, such innovations are essential for building trust and ensuring that these technologies benefit all patients equitably.