Translational AI and Deep Learning in Diagnostic Pathology

Area of Science:

• Computational pathology
• Digital pathology
• Artificial intelligence in healthcare

Background:

• Exponential growth in AI applications within healthcare and pathology.
• Innovation in deep learning technologies for cellular imaging.
• Emerging practical applications transforming diagnostic pathology.

Purpose of the Study:

• Review deep learning approaches in pathology.
• Examine public grand challenges driving AI innovation in pathology.
• Discuss emerging applications of AI in pathology.

Main Methods:

• Review of deep learning methodologies in pathology.
• Analysis of grand challenges in computational pathology.
• Survey of current and emerging AI applications in diagnostic pathology.

Main Results:

• Deep learning approaches offer transformative potential for cellular imaging and diagnostics.
• Public grand challenges have accelerated AI innovation in pathology.
• AI integration into digital pathology workflows is crucial for clinical translation.

Conclusions:

• Seamless integration of AI into digital pathology workflows is essential for clinical practice.
• AI tools can accelerate workflow, improve diagnostic consistency, and reduce errors for pathologists.
• Collaborative efforts are key to maturing AI and computational pathology for safer, more precise healthcare.