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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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

Pegah Khosravi^1,2, Thomas J Fuchs³, David Joon Ho⁴

¹Department of Biological Sciences, New York City College of Technology, City University of New York, Brooklyn, New York.

Cancer Research

|July 2, 2025

Summary

Artificial intelligence (AI) enhances cancer research by improving image analysis in radiology and pathology. AI offers explainable, reproducible diagnostic and predictive tools for better patient care and outcomes.

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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.