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) clinical decision support systems (CDSS) show promise for predicting cancer treatment outcomes. However, limited validation and clinical integration hinder widespread adoption, necessitating improvements in interpretability and ethical design.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence
Background:
- Cancer treatment outcomes vary significantly, posing challenges for traditional decision-making.
- Existing frameworks struggle to integrate complex patient data for personalized care.
- Machine learning (ML)-enabled clinical decision support systems (CDSS) offer a path toward data-driven oncology.
Purpose of the Study:
- To review the application of ML-powered CDSS in predicting cancer treatment outcomes.
- To identify ML models, data types, and predictive targets used in oncology CDSS.
- To assess the clinical implementation and validation of these systems.
Main Methods:
- Systematic literature search across six databases (2010-2025).
- Inclusion of studies deploying ML algorithms in CDSS for cancer outcome prediction.
- Data extraction and synthesis using the Mixed Methods Appraisal Tool (MMAT).
Main Results:
- 32 studies utilized ML-CDSS for predicting survival, response, toxicity, and recurrence.
- Diverse ML models (e.g., decision trees, deep learning) were employed.
- Few studies reported external validation or clinical workflow integration; interpretability and ethics were underreported.
Conclusions:
- ML-enabled CDSS demonstrate potential for enhancing cancer treatment prediction.
- Clinical adoption is limited by insufficient validation, interpretability, and ethical considerations.
- Future development must prioritize validation, interpretability, and ethical design for clinical utility.
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