Mapping the evolving landscape of artificial intelligence in pathology: A bibliometric analysis of research trends and emerging frontiers (2009-2025) - Report - MDSpire
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Mapping the evolving landscape of artificial intelligence in pathology: A bibliometric analysis of research trends and emerging frontiers (2009-2025)
Clinical Report: Analyzing the Advancements of Artificial Intelligence in Pathology
Overview
This bibliometric study analyzes AI-related research trends in computational pathology from 2009 to 2025, highlighting growth patterns and emerging research frontiers.
Background
Pathology is essential for disease diagnosis and therapeutic decision-making, yet traditional methods are labor-intensive and subject to variability. The integration of AI in computational pathology has been explored to enhance diagnostic processes.
Data Highlights
This study employs bibliometric and scientometric techniques to analyze AI-related publications in computational pathology, focusing on citation patterns and collaboration networks.
Key Findings
AI has shown strong performance in tasks such as tumor detection and classification, as evidenced by various studies.
Recent advancements include the integration of multi-modal data beyond image analysis.
The study identifies major research themes and their evolution over time.
Collaboration networks in AI research are increasingly interdisciplinary.
Challenges related to generalizability and reproducibility in AI applications are noted.
Clinical Implications
The findings provide insights into the landscape of AI in pathology.
Conclusion
This bibliometric analysis offers an overview of advancements in AI within computational pathology.
