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DigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.

In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets. 

The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control. 

The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification. 

The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional. 

Highlights
00:01 Welcome and overview of the four papers
03:02 Computational pathology and morphology-driven molecular inference
11:01 Digital cytopathology, telecytology, and QC
20:47 AI/ML in hepatocellular carcinoma precision oncology
31:04 AI literacy in veterinary education
47:42 Final takeaways and Digital Pathology 101 update 

Resources

1. Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data
   https://pubmed.ncbi.nlm.nih.gov/42052846/
   
   
2. Advances in Digital Cytopathology and Artificial Intelligence Applications
   https://pubmed.ncbi.nlm.nih.gov/42046894/
   
   
3. Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology
   https://pubmed.ncbi.nlm.nih.gov/42065059/
   
   
4. Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education
   Front Vet Sci. 2026;13:1801756 

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