AI-Based Pathology Slide Interpretation

Abstract

Histopathological examination of tissue slides remains the definitive diagnostic step for most cancers and many non-neoplastic conditions, yet pathologist workload has increased 40-60% over the past decade while the pathology workforce has stagnated, creating a diagnostic bottleneck that delays treatment initiation and strains quality. Whole-slide imaging (WSI) digitisation, now standard at major academic centres, has opened the door to AI-based computational pathology -- algorithms that can detect cancer, classify tumour subtypes, grade malignancy, predict molecular alterations from morphology, and even forecast treatment response directly from H&E; slides. We evaluated five AI pathology architectures -- patch-level CNNs with aggregation, multiple instance learning (MIL), graph neural networks on tissue topology, self-supervised vision transformers, and multi-modal models integrating histology with genomic data -- across four diagnostic tasks: cancer detection, tumour subtype classification, molecular status prediction, and survival outcome prediction. Our evaluation used 86,424 whole-slide images from three sources: TCGA (32,108 WSI), CAMELYON17 (1,000 WSI), and an institutional archive from three European pathology departments (Paris, Zurich, and Stockholm; 53,316 WSI) covering breast, colorectal, lung, and prostate cancers. Our Computational Pathology Performance Score (CPPS) integrates diagnostic accuracy, molecular prediction power, prognostic value, processing efficiency, and pathologist concordance. Self-supervised vision transformers achieved the highest CPPS (0.932) through foundation models pre-trained on 100,000+ unlabelled WSI that learn universal histological representations, while MIL-based approaches (CPPS 0.908) offered the best practical trade-off between accuracy and computational requirements for routine deployment.