The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI

Saliency maps—visual explanations showing which parts of medical images AI systems focus on—have significant inconsistencies that can undermine trust in AI diagnostic tools. The paper demonstrates that these explanations, which clinicians rely on to understand AI reasoning, may not reliably represent what the AI is actually using to make decisions.

"Black box" AI systems in healthcare pose real risks to patient safety. Without clear, consistent explanations of how AI reaches diagnostic conclusions, clinicians cannot properly validate recommendations, increasing the potential for misdiagnosis and patient harm.

Current XAI methods may create a false sense of understanding. Even when saliency maps appear to highlight relevant features in medical images, the paper suggests these visualizations can be misleading, potentially giving clinicians unwarranted confidence in AI recommendations.

Transparency in AI is not optional but essential for responsible healthcare deployment. The paper emphasizes that AI should support rather than replace clinical judgment, which requires that medical professionals can genuinely understand and critically evaluate the reasoning behind AI suggestions.

Developers and deployers of medical AI systems need to rigorously validate explanation methods before clinical use. Simply providing visual explanations is insufficient; these explanations must be tested for consistency and accuracy to ensure they genuinely reflect the AI's decision-making process.