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The study aims to explore the implementation of AI in digital pathology and its impact on clinical practice.

Promise and pragmatism of AI in global-scale digital pathology: pan-cancer approaches for clinical practice

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The study aims to explore the implementation of AI in digital pathology and its impact on clinical practice.
Evaluation of AI tools in digital pathology through systematic reviews and prospective trials across multiple cancer types.
Assessment of barriers and opportunities for AI in overcoming traditional histopathology challenges.
Focus on rigorous validation, workforce training, and integration into clinical workflows.
AI tools improve diagnostic accuracy and efficiency across various cancer subspecialties including prostate, breast, and colorectal cancer.
Significant limitations include publication bias, limited generalizability, and insufficient external validation.
Implementation barriers involve data privacy concerns and the risk of algorithmic bias exacerbating health disparities.

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Randomized trial evaluates AI's role in digital pathology, highlighting both potential benefits and challenges.

Coupland et al. (2026) studied this question.

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