AI and digital pathology advance liver disease diagnostics and transplant assessment
The study reports a comprehensive review of how digital pathology and artificial intelligence are being applied to liver disease, including hepatocellular carcinoma diagnosis and transplant management. Investigators note that while quantitative image analysis has existed for decades, recent gains in whole-slide imaging resolution and deep learning algorithms are accelerating clinical adoption. The review highlights persistent barriers such as infrastructure access, data quality standards, and regulatory guidance, while emphasizing the need for real-world validation of AI tool safety and effectiveness. These developments offer pathologists and laboratory directors new computational workflows to standardize liver tissue assessment, though widespread implementation requires further clinical evaluation.
The original study
Digital pathology, image analysis, and artificial intelligence in liver disease.
- Authors
- McGenity C, Treanor D, Slavik T, Goldin R
- Journal
- The Lancet. Digital health
- Type
- Journal Article, Review
- PMID
- 42697794
Original abstract
Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.