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<p>Artificial intelligence (AI) is transforming liver pathology by enhancing diagnostic accuracy, standardizing assessments, and supporting personalized care. This review explores current applications of AI across neoplastic and non-neoplastic liver diseases, transplant pathology, and histopathological reporting. Deep learning models have demonstrated strong performance in classifying hepatocellular carcinoma, cholangiocarcinoma, and liver metastases, as well as subtyping hepatocellular adenomas. In chronic liver diseases, AI enables continuous quantification of fibrosis and inflammation, improving reproducibility. In transplantation, algorithms assist in predicting rejection and graft viability. The pathologist plays a central role in AI tool development, validation, and clinical integration. Despite promising advances, key challenges such as data standardization, explainability, and regulatory oversight persist. Rather than replacing human expertise, AI may complement the pathologist’s role in delivering high-quality, efficient, and precise liver disease diagnosis and management.</p>

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From slide analysis to precision strategy: the pathologist in the artificial intelligence loop for liver disease diagnosis and patient management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月15日

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  • Lidiane Vieira Marins,
  • André Morales Pacca,
  • Carlos Frederico Ferreira Campos,
  • Ivanir Martins de Oliveira,
  • Geysa Bigi Maya Monteiro,
  • João Paulo Salviano Diniz e Souza,
  • Marcos Antônio Graells Perez,
  • Luan Barbosa Furtado,
  • Sonia Regina Leite Da Silva,
  • Frederico Adão de Oliveira Santana,
  • Raul S. Gonzalez

摘要

Artificial intelligence (AI) is transforming liver pathology by enhancing diagnostic accuracy, standardizing assessments, and supporting personalized care. This review explores current applications of AI across neoplastic and non-neoplastic liver diseases, transplant pathology, and histopathological reporting. Deep learning models have demonstrated strong performance in classifying hepatocellular carcinoma, cholangiocarcinoma, and liver metastases, as well as subtyping hepatocellular adenomas. In chronic liver diseases, AI enables continuous quantification of fibrosis and inflammation, improving reproducibility. In transplantation, algorithms assist in predicting rejection and graft viability. The pathologist plays a central role in AI tool development, validation, and clinical integration. Despite promising advances, key challenges such as data standardization, explainability, and regulatory oversight persist. Rather than replacing human expertise, AI may complement the pathologist’s role in delivering high-quality, efficient, and precise liver disease diagnosis and management.