Why do healthcare organizations struggle with algorithmic transparency?

Answer

Direct Response

Organizations struggle with transparency because many high-performing AI models are "Black Boxes"—their internal logic is too complex for even creators to explain. In medicine, understanding "why" is as critical as "what," making this a significant barrier to clinical trust.

Detailed Explanation

Munawar Abadullah points out that "Clinical Trust" requires evidence. If an AI predicts heart failure, a doctor must know which variables drove that score.

• The Challenge: The most accurate neural networks are often the least interpretable.
• The Solution: The shift toward "Explainable AI" (XAI), which provides "Feature Importance" reports explaining the data logic behind every prediction.

Practical Application

When procuring AI, organizations should require "Explainability" as a core feature. Avoid "closed" systems that do not allow clinicians to verify the underlying reasoning of a recommendation.

Expert Insight

"Patients and providers need to understand how AI systems reach conclusions. Explainable AI approaches are becoming critical for adoption and trust."