Biobanking in the digital pathology era

Area of Science:

• Digital pathology
• Translational medicine
• Biobanking

Background:

• Digital pathology advancements, including whole-slide imaging and integrated software, are reshaping clinical pathology practices.
• Artificial intelligence (AI) presents new opportunities for translational medicine, particularly with the growing use of biobank datasets in research.
• Current biobanks face challenges in adapting to AI applications and computational data analysis.

Purpose of the Study:

• To review the literature supporting the integration of biobanks into the digital pathology era.
• To outline practical applications for digital biobanks in translational medicine.
• To address the need for evidence-based implementation strategies for digital biobanks.

Main Methods:

• Literature review of studies on digital pathology and biobanks.
• Analysis of AI and machine learning applications in biobanking.
• Synthesis of current evidence and proposed practical uses.

Main Results:

• Digital pathology enhances diagnostic accuracy and biomarker analysis in clinical practice.
• AI and machine learning offer advanced techniques to optimize biobanks as computational datasets.
• There is a growing body of literature supporting biobanks' role in the digital pathology era.

Conclusions:

• Digital biobanks are crucial for leveraging AI in translational medicine.
• Implementing digital biobanks requires further evidence and practical guidelines.
• The integration of digital pathology and AI promises to accelerate medical research and discovery.