Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems.
A review identified 32 studies on machine learning for cancer outcome prediction.
Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems.
Cancer treatment poses significant challenges due to variability in patient responses, disease progression, and therapy outcomes.
This scoping review aimed to explore how machine learning-powered clinical decision support systems are applied to predict treatment outcomes across different types of cancer.
A systematic search was conducted across six databases covering studies from 2010 to 2025.
Predictive objectives ranged from survival estimation and therapy response to toxicity risk and recurrence detection.
Machine learning techniques varied from decision trees and vector machines to deep learning models such as convolutional neural networks.
While technical performance was promising, few studies demonstrated external validation or integration into clinical workflows.
Interpretability, ethical considerations, and patient involvement were frequently underreported.
Machine learning-enabled clinical decision support systems have shown significant potential in predicting cancer treatment outcomes, yet their adoption in practice remains limited.
Advancing these systems requires focus on validation, interpretability, data integration, and ethical design to bridge the gap between innovation and clinical utility.