Assessing Artificial Intelligence Models to Diagnose and Differentiate Common Liver Carcinomas

Abstract

The role of artificial intelligence (AI) in health care delivery is growing rapidly. Due to its visual nature, the specialty of anatomic pathology has great promise for applications in AI. We examine the potential of six different AI models for differentiating and diagnosing the three most common primary liver tumors: hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and combined HCC and CCA (cHCC/CCA). Our results demonstrated that for all three diagnoses, the sensitivity, specificity, positive predictive value, and negative predictive value was greater than or equat to 94% in the best model tested, with results greater than or equat to 92% in all categories in three of the models. These values are comparable to interpretation by general pathologists alone and demonstrate AI's potential in interpreting patient specimens for primary liver carcinoma. Applications such as these have multiple implications for delivering quality patient care, including assisting with intraoperative consultations and providing a rapid "second opinion" for confirmation and increased accuracy of final diagnoses. These applications may be particularly useful in underserved areas with shortages of subspecialized pathologists or after hours in larger medical centers. In addition, AI models such as these can decrease turnaround times and the inter- and intra-observer variability well documented in pathologic diagnoses. AI offers great potential in assisting pathologists in their day-to-day practice.