Using AI to Enhance Cancer Diagnostics and Treatment

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David Craig, PhD, discusses how AI is transforming cancer histopathology.

AI is transforming histopathology by enhancing image analysis, aiding diagnosis, optimizing workflows, and accelerating research. In an interview with Targeted OncologyTM, David Craig, PhD, professor and chair in the Department of Integrative Translational Science at City of Hope, discusses this exciting field.

In image analysis, AI automates the detection, quantification, segmentation, and classification of tissue features in digital slides, improving efficiency and reducing manual workload. It assists in diagnosis by acting as a second opinion, identifying subtle patterns, and integrating multi-omics data for a more comprehensive understanding of diseases like cancer.

Workflow optimization is achieved through AI-powered case prioritization and automation of routine tasks, leading to faster turnaround times. In research, AI facilitates biomarker discovery and contributes to personalized medicine by predicting treatment responses based on tissue analysis.

Examples of AI applications include cancer diagnosis and grading, metastasis detection, immunohistochemistry analysis, molecular biomarker prediction, and rare disease diagnosis.

Despite challenges such as data requirements, generalizability, regulatory hurdles, ethical considerations, and workflow integration, AI holds significant promise. It augments pathologists' expertise, potentially leading to more accurate diagnoses and improved patient care in the evolving field of histopathology.

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