Machine Learning in oncology: A clinical appraisal

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Computational Biology

Background:

• Machine learning (ML) algorithms learn from data without explicit programming.
• Advancements in digital health records and medical imaging have increased data availability.
• ML shows significant promise across various medical applications, particularly in oncology.

Purpose of the Study:

• To review the applications of machine learning in oncology.
• To highlight ML's role in improving cancer diagnosis and treatment.
• To discuss the future impact of ML on precision medicine in cancer care.

Main Methods:

• Review of existing literature on ML applications in oncology.
• Analysis of ML algorithms used for tasks like risk assessment, segmentation, and prognosis prediction.
• Synthesis of findings regarding ML's current and potential contributions to cancer management.

Main Results:

• ML algorithms are applied to oncological risk assessment, lesion detection, and staging.
• ML aids in predicting patient prognosis and response to therapy.
• ML tools are increasingly integrated into medical image analysis for cancer detection.

Conclusions:

• Machine learning is a transformative technology in oncology.
• ML facilitates personalized treatment strategies and improves patient outcomes.
• The integration of ML is crucial for advancing precision medicine in cancer care.