Artificial Intelligence and Deep Learning Technique for Risk Assessment and Early Prediction of Heart and Kidney Cancer Detection

Abstract

Artificial Intelligence and Deep Learning approaches are promising in early diagnosis and risk assessment of kidney and heart malignancies through medical data analysis: imaging, genetics, and clinical information. Through AI models and deep learning algorithms such as CNN, artificial intelligence has the ability to find minute patterns and irregularities in medical images that human clinicians might miss. This study is focused on using deep learning and artificial intelligence techniques to assess the risk of kidney and heart cancer, as well as early diagnosis. Compared to conventional methods, the primary objectives were to determine the extent to which AI models enhanced diagnostic precision, sensitivity, and specificity. Using CNNs for ECG data to diagnose heart cancer and CT and MRI scans to diagnose kidney cancer. The study addressed the issues of data quality, model transparency, and integration with clinical workflows, while demonstrating that AI and DL do bear potential for improving early cancer detection and allowing for more personalized treatment plans. This research opens the door to even more accurate and effective diagnostic tools in the clinical setting, revolutionizing oncology through the power of AI.