Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems
Adib Hossain1, Md Mohaimin Rashid2, Towsif Alam3
1Department of Business Analytics, Trine University, IN, USA.
Digital Health
|July 22, 2026
Summary
Machine learning (ML)-enabled clinical decision support systems (CDSS) show promise for predicting cancer treatment outcomes. However, limited clinical implementation highlights the need for improved validation, interpretability, and ethical considerations.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence
Background:
- Cancer treatment outcomes vary significantly, necessitating advanced decision-making tools.
- Traditional frameworks struggle with complex data integration, driving the need for intelligent systems.
- Machine learning (ML)-enabled clinical decision support systems (CDSS) offer personalized, predictive oncology care.
Purpose of the Study:
- To review the application of ML-powered CDSS in predicting cancer treatment outcomes.
- To identify ML models, data types, predictive targets, and clinical implementation of these systems.
Main Methods:
- Systematic search of six databases (2010-2025) for studies using ML algorithms in oncology CDSS for outcome prediction.
- Data extraction and synthesis using a structured charting process and narrative categorization.
- Study quality assessed using the Mixed Methods Appraisal Tool (MMAT).
Main Results:
- 32 studies were included, utilizing diverse ML models (e.g., decision trees, deep learning) for predicting survival, response, toxicity, and recurrence.
- Promising technical performance was noted, but external validation and clinical workflow integration were limited.
- Interpretability, ethical concerns, and patient involvement were often underreported.
Conclusions:
- ML-enabled CDSS demonstrate substantial potential for predicting cancer treatment outcomes.
- Clinical adoption is hindered by challenges in validation, interpretability, data integration, and ethical design.
- Future development must prioritize these areas to enhance clinical utility and bridge the innovation-practice gap.
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