id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-10162	Herrera-Moya, Pedro Aquino; Peralta-Gamboa, Dennis Alfredo	Future trends of AI in precision oncology: Insights from a systematic review and evidence-based roadmap (2021–2024)	2025	11	.pdf	application/pdf	5707	301	35	AI models, including machine learning and deep learning, have demonstrated significant improvements in diagnostic accuracy, treatment planning, and personalized therapies. Cancer Type AI Model Performance Metrics Clinical Application Breast Cancer CatBoost + MLP Neural Network AUC: 0.98, Sens: 98.06% Tissue classification Colorectal Cancer Machine Learning (dMMR) AUC: 0.832 Pre-surgical detection Skin Cancer Convolutional Neural Network Improvement: 15.6% in accuracy Skin tumor classification Liver Cancer AI-based Proteomics AUC: 0.988 Treatment response (sorafenib) Brain Cancer U-Net Architecture Dice Score: 91.38% Brain tumor segmentation Stomach Cancer Deep CNN Sens: 92.08% Early diagnosis Below is a summary of the most relevant clinical applications of AI models in oncology, along with key findings that demonstrate their impact on improving cancer diagnosis and treatment (see Table 2).	cache/easat-10162.pdf	txt/easat-10162.txt