Abstract

Cancer is a global health concern, and early detection plays a crucial role in improving patient outcomes and reducing mortality rates. In recent years, artificial intelligence (AI) techniques have emerged as promising tools for early cancer detection. This systematic review aims to provide an overview of the current state of research on using AI for early detection of cancer. The review begins by presenting an overview of the various types of cancers and their diagnostic challenges. It then delves into the potential of AI in cancer detection, discussing different AI techniques such as machine learning, deep learning, and image analysis. The review also examines the sources of data used in AI-based cancer detection, including medical images, genomic data, electronic health records, and other clinical data. Furthermore, the review highlights the significant advancements made in AI-based cancer detection across different cancer types, such as breast, lung, prostate, colorectal, and skin cancer. It discusses the specific algorithms and models developed for each cancer type and their performance in terms of sensitivity, specificity, and accuracy. The review also addresses the challenges and limitations associated with AI-based cancer detection, including data availability, standardization, interpretability, and regulatory aspects. Moreover, it explores the ethical considerations and potential biases that may arise in implementing AI systems for cancer detection. Lastly, the review discusses the future directions and potential implications of AI in early cancer detection. It emphasizes the need for large-scale clinical trials and collaborations between healthcare professionals, researchers, and AI experts to validate and implement AI models in clinical practice.