Future trends of AI in precision oncology: Insights from a systematic review and evidence-based roadmap (2021–2024) - Herrera-Moya, Pedro Aquino; Peralta-Gamboa, Dennis Alfredo (Edelweiss Applied Science and Technology, 2025)
This systematic review investigates the advancements and challenges of artificial intelligence (AI) in precision oncology, focusing on research from 2021 to 2024, to provide an evidence-based roadmap for future implementation. Following the PRISMA guidelines, a comprehensive search was conducted across Scopus, SciELO, and Google Scholar using relevant keywords to identify studies evaluating AI applications in cancer diagnosis and treatment. Eighteen relevant articles were selected and qualitatively analyzed to identify key themes and patterns. AI models, including machine learning and deep learning, have demonstrated significant improvements in diagnostic accuracy, treatment planning, and personalized therapies. Examples include a hybrid CatBoost-MLP model that achieved 98.06% accuracy in breast tissue classification and a deep convolutional neural network with 92.08% sensitivity for early gastric cancer detection. AI also reduces radiotherapy planning times, enhancing accessibility, particularly in developing countries. The integration of AI into oncology has transformative potential, enhancing diagnostic precision, risk stratification, and personalized treatment strategies. However, challenges remain, including data standardization, the need for diverse datasets, and ethical considerations. This study highlights the need for robust AI models, international data standards, and ethical frameworks to ensure the safe, equitable, and effective clinical implementation of AI in oncology, paving the way for improved patient outcomes and healthcare accessibility.