Leveraging digital pathology and AI to transform clinical diagnosis in developing countries.

Area of Science:

• Digital pathology and computational pathology
• Artificial intelligence (AI) in medical diagnostics

Background:

• Computational pathology offers enhanced diagnostic accuracy and efficiency.
• Digital pathology and AI are central to this transformation, enabling objective analysis and large-scale data handling.

Purpose of the Study:

• To explore the potential of computational pathology in transforming diagnostics and treatment planning.
• To identify and address translation barriers hindering the adoption of computational pathology, particularly in resource-limited settings.

Main Methods:

• Review of current advancements in digital pathology and AI for diagnostic applications.
• Identification of key challenges including standardization, dataset availability, and computational expertise.

Main Results:

• Computational pathology provides objective, precise diagnoses and increases efficiency.
• Significant barriers include lack of standardized image acquisition/analysis and limited annotated datasets.
• A need for interdisciplinary collaboration and technological innovation is highlighted.

Conclusions:

• Addressing standardization and dataset creation is critical for computational pathology's success.
• Collaborative efforts and technological advancements can improve patient outcomes and reduce costs.
• Computational pathology can be a valuable tool in resource-limited healthcare settings.