Keywords
Medical Imaging
Patient Education
Attention Mechanisms
Surgical Robotics
How to Cite
Abstract
The burgeoning field of medical imaging has revolutionized healthcare by enabling non-invasive visualization of internal organs and structures. However, the intricate nature of these images often demands specialized training for accurate interpretation. Artificial intelligence (AI), particularly deep learning, has shown remarkable success in analyzing medical images and assisting with diagnoses. However, these models often function as "black boxes," lacking transparency in their decision-making processes. This opacity hinders trust in AI-driven results and limits their clinical utility.
Explainable AI (XAI) techniques bridge this gap by providing insights into the rationale behind AI models' predictions in medical imaging. By demystifying AI's reasoning, XAI fosters trust and collaboration between healthcare professionals and AI systems. This paper delves into the application of XAI for interpreting medical imaging reports.
We begin by highlighting the advantages of AI in medical image analysis, encompassing efficient analysis, improved accuracy, and the potential for early disease detection. However, we emphasize the limitations of black-box AI models, including the lack of transparency, potential for bias, and difficulty in debugging errors.
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