Abstract:
The integration of artificial intelligence (AI) into healthcare holds significant
potential for enhancing colon cancer detection, prediction, and patient care. AI can
significantly improve decision-making processes, particularly in the diagnosis and
prognosis of colon cancer. This review focuses on explainable artificial intelligence
(XAI), which enhances the interpretability and transparency of AI models, facilitating
in-depth disease analysis. By leveraging XAI, this study delves into the complexities of
colorectal cancer, emphasizing early detection, risk assessment, and clinical
decision-making. The review critically examines existing literature on XAI applications in
colorectal cancer, highlighting both the benefits and limitations. It addresses key
challenges such as data privacy, model transparency, and regulatory compliance,
emphasizing the necessity for robust patient-provider communication to foster trust.
Additionally, the study explores ethical and legal considerations, ensuring fair and
unbiased AI implementation. Advancements in predictive modeling and interpretive
techniques like SHAP (Shapley Additive exPlanations) are discussed, demonstrating
their potential in identifying biomarkers and improving patient outcomes through
personalized medicine. The review underscores the importance of mitigating biases in
AI models, promoting equity in clinical decision-making. Furthermore, this analysis
highlights the evolving landscape of AI in healthcare, showcasing significant
improvements in areas such as imaging assessment and risk prediction. It also delves
into the architecture of various AI models like VGG-16, ResNet50, and InceptionV3,
providing a comparative analysis of their accuracy in colorectal cancer detection.
Ultimately, this comprehensive analysis of XAI in colorectal cancer aims to bridge the
gap between technological innovation and clinical application. By offering insights into
the challenges and opportunities presented by XAI, the study seeks to inform future
research and policy development, enhancing the overall effectiveness of colon cancer
care and contributing to improved patient outcomes.