Overcoming the challenges to implementation of artificial intelligence in pathology.

Area of Science:

• Digital pathology
• Computational pathology
• Artificial intelligence in medicine

Background:

• Pathologists face increasing workloads, impacting patient care quality.
• Artificial intelligence (AI) offers potential to enhance diagnostic accuracy and biomarker discovery from digital slides.
• The adoption of AI in pathology lags behind other medical fields like radiology.

Purpose of the Study:

• To critically review advancements in digital and computational pathology over the past decade.
• To identify and address key challenges hindering AI integration in pathology.
• To offer a future perspective on AI-enabled precision oncology.

Main Methods:

• Review of developments in digital and computational pathology over the last 10 years.
• Analysis of hurdles impacting AI adoption in pathology.
• Formulation of strategies to overcome identified challenges.

Main Results:

• Significant progress in AI applications for pathology, including improved diagnostics and biomarker extraction.
• Persistent challenges in AI implementation, such as workflow integration and regulatory hurdles.
• Identification of pathways to accelerate AI adoption for enhanced patient care.

Conclusions:

• AI holds substantial promise for democratizing expert pathology and advancing precision oncology.
• Addressing current barriers is essential for realizing the full potential of AI in pathology.
• Future integration of AI is expected to revolutionize oncological diagnostics and treatment strategies.