Accuracy of Decision Support Systems for Breast Cancer - Initial Results
Priscyla Waleska Simões1, Ronaldo Borges Vicente1, Patrícia Duarte Simões Pires1
1Grupo de Pesquisa em Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense, Brazil.
Studies in Health Technology and Informatics
|January 4, 2018
Summary
This study systematically reviewed decision support systems for breast cancer diagnosis accuracy. It identified 183 articles for full review after screening 622 references to assess diagnostic performance.
Area of Science:
- Medical Informatics
- Oncology
- Diagnostic Accuracy Research
Background:
- Accurate breast cancer diagnosis is crucial for effective treatment.
- Decision support systems (DSS) show potential in improving diagnostic accuracy.
- Evaluating the real-world performance of these systems is essential.
Purpose of the Study:
- To systematically review and meta-analyze the diagnostic accuracy of decision support systems in breast cancer detection.
- To assess the reliability and effectiveness of AI-driven tools in clinical breast cancer diagnosis.
Main Methods:
- Systematic review and meta-analysis methodology.
- Independent screening of 622 references by two researchers.
- Selection of 183 articles for full-text review based on consensus.
Main Results:
- The initial screening identified a substantial body of literature on DSS for breast cancer.
- A rigorous selection process ensured the inclusion of relevant diagnostic accuracy studies.
- Further analysis will quantify the overall accuracy of these systems.
Conclusions:
- Decision support systems are increasingly studied for breast cancer diagnosis.
- A systematic approach is vital for evaluating the accuracy of these emerging technologies.
- The findings will inform the clinical integration of reliable diagnostic support tools.
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