JMIR Publications Highlights Growing Disconnect Between AI Transparency Laws and Practical Patient Understanding in Clinical Care

JMIR Publications examines why current AI transparency laws often fail to provide patients with meaningful medical explanations in clinical settings.

By: AXL Media

Published: Mar 23, 2026, 9:57 AM EDT

Source: Information for this report was sourced from JMIR Publications.

The Legal Right to an Explanation in the Age of Algorithms

As artificial intelligence becomes a foundational tool in medical diagnostics and imaging, the legal landscape is struggling to keep pace with technical realities. According to a new article by JMIR Correspondent Anshu Ankolekar, the European Union’s AI Act has established a legal basis for transparency, yet the "right to understand" remains a theoretical concept for most patients. When a high-risk AI system assists in a diagnosis, patients are increasingly asking for the reasoning behind the computer's conclusion—a request that even experienced clinicians often struggle to fulfill accurately.

The Interpretability Trade-off: Accuracy vs. Transparency

One of the primary hurdles identified in the JMIR analysis is the inherent "interpretability trade-off." The most advanced AI models, which offer the highest diagnostic accuracy, often operate through millions of parameters that are impossible for a human to fully trace. If regulators force the use of simpler, more "explainable" models to satisfy legal transparency requirements, they risk sacrificing the very diagnostic precision that ensures patient safety. This creates a direct conflict between the patient's right to know and their right to the best possible medical outcome.

Automation Bias and the Erosion of Independent Assessment

The report also raises concerns about "automation bias," a phenomenon where clinicians may defer to an algorithm's suggestion even when it contradicts their own professional judgment. Research indicates that incorrect AI suggestions can pull medical staff toward an incorrect diagnosis regardless of their experience level. Consequently, an explanation delivered to a patient by a clinician who has already deferred to an algorithm may not reflect a truly independent clinical assessment, further complicating the transparency of the decision-making process.