Diagnosis of Cancer Using Artificial Intelligence Modeling tools: A Comprehensive Review
Year: 2024
Type: article
Abstract: Artificial Intelligence (AI) has emerged as a transformative force in cancer diagnostics, offering significant advancements in accuracy, efficiency, and scalability across various cancer types, including breast, lung, thyroid, and colorectal cancers. This comprehensive review explores the application of AI technologies such as Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNNs), and Natural Language Processing (NLP) in medical imaging, pathology, and risk prediction. AI tools have demonstrated remarkable capabilities in detecting early-stage malignancies, reducing false positives and false negatives, and integrating multi-modal data to provide holistic diagnostic insights. Examples such as AI-assisted mammography, lung nodule detection via CT scans, thyroid nodule classification through ultrasound, and polyp identification in colorectal cancer screening highlight AI's contributions to improved cancer detection and personalized treatment plans. Despite these advancements, challenges remain, including data privacy, algorithm bias, regulatory compliance, and integration into clinical workflows. This review underscores the potential of AI to revolutionize cancer diagnostics while emphasizing the need for further research, transparency, and standardized implementation protocols.
Source: Nanotechnology Perceptions
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7217557767