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Updated: Apr 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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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
PubMed
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This summary is machine-generated.

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.

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  • 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.