Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility,...

Area of Science:

• Computational research
• Data science
• Machine learning/Artificial intelligence in oncology

Background:

• Artificial intelligence (AI) integration is revolutionizing cancer research, particularly in radiology, pathology, and multimodal data analysis.
• Current manual diagnostic and predictive tasks in cancer care suffer from low reproducibility.
• AI offers standardized, explainable methods to assist clinicians in decision-making.

Purpose of the Study:

• To review state-of-the-art artificial intelligence methods applied to cancer research.
• To highlight AI's role in image classification, segmentation, multiple instance learning, generative models, and self-supervised learning.
• To discuss the impact of AI in radiology, pathology, and multimodal data integration for cancer diagnosis and treatment.

Main Methods:

• Review of current artificial intelligence techniques and their applications in cancer research.
• Focus on AI methods including image classification, segmentation, multiple instance learning, generative models, and self-supervised learning.
• Exploration of AI integration in radiology, pathology, and multimodal data analysis (genomics).

Main Results:

• AI significantly enhances tumor detection, diagnosis, and treatment planning in radiology.
• AI-driven pathology image analysis improves cancer detection, biomarker discovery, and diagnostic consistency.
• Multimodal AI approaches integrate diverse data for comprehensive diagnostic insights.

Conclusions:

• AI demonstrates transformative potential in improving patient outcomes and advancing cancer care.
• Explainable AI methods are crucial for clinical decision support and enhancing reproducibility.
• Continued development and integration of AI are essential for future cancer discoveries.