AI in Oncology: Enhancing Diagnostic Accuracy and Prediction with Machine Learning: A Systematic Review

Abstract

Artificial intelligence (AI) has shown noteworthy potential in the diagnosis and management of malignant tumors. This systematic review summarizes the findings of several studies that investigated the application of AI in various aspects of Malignant Tumors management. The studies evaluated the performance of AI systems including machine learning and deep learning algorithms, in predicting resection outcomes in ovarian cancer, detecting malignant melanoma from histopathological sections, classifying skin cancer, and diagnosing melanoma. The results demonstrated that AI systems have comparable or superior performance compared to human experts in certain tasks, suggesting their potential to improve diagnostic accuracy and prediction. Patient attitudes towards AI in dermatology were generally positive with expectations of faster, more precise, and unbiased diagnostics. However, concerns about data protection, impersonality, and errors associated with AI need to be addressed. Integrating AI with human expertise has been shown to enhance the accuracy and reliability of skin cancer classification. AI-assisted CT imaging has demonstrated improved diagnostic accuracy and speed in pulmonary nodules, while the combination of AI with Raman spectroscopy has shown potential in breast cancer diagnosis. Continued collaboration between AI systems and healthcare professionals has the potential to revolutionize Malignant Tumors diagnosis and management, leading to improved patient outcomes and more efficient healthcare delivery.