CADx – Enhancing confidence in AI-assisted healthcare

Our Enhanced CADx Framework supports clinicians with AI-driven diagnoses that are transparent, interpretable, and easy to validate. Rather than offering black-box outputs, the CADx system provides multiple explanation types, both visual and concept-based, within a single intuitive interface. Clinicians can swiftly explore the rationale behind AI diagnoses, reducing the time spent on manual verification and allowing them to focus on patient care. By clarifying AI reasoning, the enhanced CADx system reduces workload and strengthens trust, thereby enhancing confidence in AI-assisted healthcare.

Challenge

Traditional CADx systems often function as “black boxes”, providing diagnoses without clear reasoning. Clinicians are left with minimal insight into the reasoning behind a diagnosis, necessitating extra time for manual validation before they can trust the AI’s output. This opacity not only increases workload but also diminishes clinician confidence in AI-driven healthcare. Furthermore, existing explanation methods are frequently disjointed, either focusing solely on heatmaps or singular techniques, making it challenging for clinicians to achieve a comprehensive, clinically meaningful understanding of AI decisions.

Solution

Our enhanced CADx system integrates multiple explanation methods, both visual and concept-based, into a single user-friendly interface. Instead of depending on a solitary form of explainability, the system offers clinicians complementary perspectives, such as visual heatmaps (Grad-CAM) and concept-based reasoning (TCAV), thereby clarifying AI reasoning and facilitating validation.

Key Benefits:

• Improves trust and reliability in AI-assisted diagnosis
• Provides clinicians with concept-level, visual, and quantitative explanations
• Ensures diagnostic outputs align with explanation modules
• Facilitates more effective clinician-AI collaboration in diagnostic workflows

Insight Background

The enhanced CADx system addresses the pivotal question: “How can we render a black-box AI’s reasoning transparent and comprehensible to human experts, thus fostering trust and improving their collaborative capacity with the system?”

By translating AI logic into clinical concepts, providing a multifaceted view, and enabling validation through an interactive user interface, our system elevates AI from a mere diagnostic tool to a collaborative partner in healthcare.