Review on colorectal cancer biomarkers

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.