@article{uneatlantico27915,
           month = {Marzo},
           title = {A Systematic Literature Review on Integrated Deep Learning and Multi-Agent Vision-Language Frameworks for Pathology Image Analysis and Report Generation},
          author = {Usama Ali and Imran Shafi and Jamil Ahmad and Arlette Z{\'a}rate C{\'a}ceres and Thania Chio Montero and Hafiz Muhammad Raza ur Rehman and Imran Ashraf},
            year = {2026},
         journal = {Computational and Structural Biotechnology Journal},
             url = {http://repositorio.uneatlantico.es/id/eprint/27915/},
        keywords = {Deep learning, vision-language models, pathology image analysis, whole-slide imaging, multi-agentsystems, automated report generation, large language models},
        abstract = {This systematic literature review (SLR) investigates the integration of deep learning (DL), vision-language models(VLMs), and multi-agent systems in the analysis of pathology images and automated report generation. The rapidadvancement of whole-slide imaging (WSI) technologies has posed new challenges in pathology, especially due to thescale and complexity of the data. DL techniques in general and convolutional neural networks (CNNs) and transform-ers in particular have signi?cantly enhanced image analysis tasks including segmentation, classi?cation, and detection.However, these models often lack generalizability to generate coherent, clinically relevant text, thus necessitating theintegration of VLMs and large language models (LLMs). This review examines the e?ectiveness of VLMs and LLMsin bridging the gap between visual data and clinical text, focusing on their potential for automating the generationof pathology reports. Additionally, multi-agent systems, which leverage specialized arti?cial intelligence (AI) agentsto collaboratively perform diagnostic tasks, are explored for their contributions to improving diagnostic accuracy andscalability. Through a synthesis of recent studies, this review highlights the successes, challenges, and future direc-tions of these AI technologies in pathology diagnostics, o?ering a comprehensive foundation for the development ofintegrated, AI-driven diagnostic work?ows.}
}