Artificial intelligence in surgical pathology – Where do we stand, where do we go?

Abstract

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determinants of tumor entities and sub-entities. This is a task where artificial intelligence (AI)/machine learning (ML) systems could significantly contribute to help with tumor outcome prediction and the search for new diagnostic or treatment stratification biomarkers. AI systems are increasingly integrated into routine pathology workflows to improve accuracy, reproducibility, productivity and to reveal difficult-to-see features in complicated histological slides, including the quantification of important markers for tumor grading and staging. In this article, we review the infrastructure needed to facilitate digital and computational pathology. We address the barriers for its full deployment in the clinical setting and describe the use of AI in intraoperative or postoperative settings were frozen or formalin-fixed, paraffin-embedded materials are used. We also summarize quality assessment issues of slide digitization, new spatial biology approaches, and the determination of specific gene-expression from whole slide images. Finally, we highlight new innovative and future technologies, such as large language models, optical biopsies, and mass spectrometry imaging.

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