Role of Artificial Intelligence in High Throughput Diagnostics for Colorectal Cancer Current Updates

Abstract

The existence of cancer has been stated as a century’s oldest challenge for the entire human race around the
globe recording a large amount of mortality per year and as per the WHO data nearly 10 million deaths were reported in 2021 worldwide besides others. Colorectal cancer is considered a major threat as this is cancer-related to the colon and rectum with an incidence of 41/1,00,000 recorded annually to overcome this challenge our medical system requires more advanced, accurate and efficient high throughput techniques for the prognosis and effective treatment of this disease. Artificial intelligence’s role in healthcare has been a matter of discussion among experts over the past few years, but more recently the spotlight has focused more specifically on the role that this technology can play in improving patient outcomes and improving the effectiveness of diagnosis and treatment processes. Artificial intelligence refers to a broad category of technologies, including machine learning, natural language processing and deep learning. Exploration of Molecular pathways with characteristics that helps in subtyping of Colorectal Cancer (CRC) leading to specific treatment response or prognosis, for the effective treatment, classification and early detection done using Artificial Intelligence based technologies have shown promising results so far, that it may be utilized to create prediction models in the current environment to distinguish between polyps, metastases, or normal cells in addition to early detection and effective cancer therapy. Nowadays many scientists are putting effort into designing such fabricating models by combining natural language processes and deep learning that can differentiate between non-adenomatous and adenomatous polyps to identify hyper-mutated tumours, genetic mutations and molecular pathways known as IDaRS strategy or iterative draw-and-rank sampling. The review study primarily focuses on the significance of emerging AI-based approaches for the diagnosis, detection, and prognosis of colorectal cancer in light of existing obstacles.