The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in
  XAI
Anna Stubbin Thompson Chyrikov Jim Zhao Christina Chajo
Explainable artificial intelligence (XAI) plays an indispensable role in
demystifying the decision-making processes of AI, especially within the
healthcare industry. Clinicians rely heavily on detailed reasoning when making
a diagnosis, often CT scans for specific features that distinguish between
benign and malignant lesions. A comprehensive diagnostic approach includes an
evaluation of imaging results, patient observations, and clinical tests. The
surge in deploying deep learning models as support systems in medical
diagnostics has been significant, offering advances that traditional methods
could not. However, the complexity and opacity of these models present a
double-edged sword. As they operate as "black boxes," with their reasoning
obscured and inaccessible, there's an increased risk of misdiagnosis, which can
lead to patient harm. Hence, there is a pressing need to cultivate transparency
within AI systems, ensuring that the rationale behind an AI's diagnostic
recommendations is clear and understandable to medical practitioners. This
shift towards transparency is not just beneficial -- it's a critical step
towards responsible AI integration in healthcare, ensuring that AI aids rather
than hinders medical professionals in their crucial work.