Challenges and Barriers in Implementing AI for Clinical Applications in Anatomic Pathology

Area of Science:

• Digital pathology and computational diagnostics.
• Artificial intelligence (AI) and machine learning applications in pathology.

Background:

• The shift to digital workflows in anatomic pathology enables advanced AI-driven diagnostics.
• AI systems can perform tumor detection, grading, prognostication, and molecular inference from slides.

Purpose of the Study:

• To review current evidence on digital pathology and AI applications.
• To examine barriers to clinical adoption of AI in anatomic pathology.
• To provide recommendations for developing clinically deployable AI systems.

Main Methods:

• Synthesis of current evidence on digital pathology and AI.
• Analysis of technical, organizational, and regulatory impediments to AI adoption.
• Formulation of practical recommendations for AI system development.

Main Results:

• AI shows promise in tumor detection, grading, prognostication, and biomarker assessment.
• Clinical translation is limited by data heterogeneity, validation challenges, and workflow integration issues.
• Concerns regarding transparency, accountability, and professional trust also hinder adoption.

Conclusions:

• Realizing AI's full potential in anatomic pathology requires addressing data, validation, and infrastructure challenges.
• Robust digital infrastructure, representative datasets, rigorous validation, and coordinated governance are essential.
• Overcoming these barriers will facilitate the clinical integration of AI in pathology diagnostics.