Cancer stands out as one of the most pressing global health challenges, and over the past decade, significant advancements have been made in diagnostic tests and methodologies. These tests fall into categories such as imaging tests, and endoscopic procedures, generating substantial volumes of data. This data needs expert evaluation to distinguish between benign and malignant tumors. Enter artificial intelligence (AI), which offers improved accuracy in analyzing large quantities of diagnostic imaging, thereby enhancing the efficiency of healthcare systems. The integration of new AI algorithms, technical advances, and enhanced computer hardware enables the training of diagnostic neural networks. This allows machines to learn from a diverse range of scans, leading to a comprehensive understanding of cancer scanning data. AI software has been evaluated against conventional diagnostic tools used by cancer specialists, and the results show significantly increased precision, making it highly effective in early diagnosis and extended forecasting for various types of cancers. In the realm of breast cancer prognosis, AI systems have demonstrated the potential to surpass human specialists, enabling much earlier diagnosis. Similarly, informatics has developed AI algorithms and deep learning techniques capable of predicting individuals' likelihood of developing lung cancer through low-dose CT analysis. The use of convolutional neural networks (CNNs) has been instrumental in diagnosing the invasion depth of gastric cancer based on gastric endoscopy.
Keywords
Cancer, Diagnostic Tests, Artificial Intelligence, Deep Learning Techniques.
