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A Systematic Literature Review on Integrated Deep Learning and Multiagent Vision-Language Frameworks for Pathology
Usama Ali1, Imran Shafi1,2, Jamil Ahmad2,3
1College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Computational and Structural Biotechnology Journal
|April 15, 2026
Summary
Deep learning (DL), vision-language models (VLMs), and multiagent systems are revolutionizing pathology image analysis and automated report generation. Integrating these AI technologies enhances diagnostic accuracy and efficiency in whole-slide imaging (WSI).
Area of Science:
- Artificial Intelligence in Pathology
- Computational Pathology
- Digital Pathology
Background:
- Whole-slide imaging (WSI) presents challenges in pathology due to data scale and complexity.
- Deep learning (DL) models, including CNNs and transformers, have improved pathology image analysis.
- Existing DL models struggle with generating clinically relevant text for reports.
Purpose of the Study:
- To review the integration of DL, VLMs, and multiagent systems for pathology image analysis and automated report generation.
- To assess the effectiveness of VLMs and LLMs in connecting visual pathology data with clinical text.
- To explore the role of multiagent systems in enhancing diagnostic accuracy and scalability.
Main Methods:
- Systematic literature review of recent studies.
- Synthesis of research on deep learning, vision-language models, and multiagent systems in pathology.
- Analysis of AI applications in whole-slide imaging analysis and report generation.
Main Results:
- DL techniques significantly enhance pathology image analysis tasks like segmentation and classification.
- VLMs and LLMs show promise in bridging the gap between visual data and clinical text for report automation.
- Multiagent systems contribute to improved diagnostic accuracy and scalability in AI-driven pathology.
Conclusions:
- Integrated AI approaches, combining DL, VLMs, and multiagent systems, offer a robust framework for advanced pathology diagnostics.
- These technologies are crucial for developing efficient, AI-driven diagnostic workflows in digital pathology.
- Further research is needed to address challenges and optimize the application of these AI tools in clinical practice.
