Search for: All records

Surgical pathology reports contain essential diagnostic information, in free-text form, required for cancer staging, treatment planning, and cancer registry documentation. However, their unstructured nature and variability across tumor types and institutions pose challenges for automated data extraction. We present a consensus-driven, reasoning-based framework that uses multiple locally deployed large language models (LLMs) to extract six key diagnostic variables: site, laterality, histology, stage, grade, and behavior. Each LLM produces structured outputs with accompanying justifications, which are evaluated for accuracy and coherence by a separate reasoning model. Final consensus values are determined through aggregation, and expert validation is conducted by board-certified or equivalent pathologists. The framework was applied to over 4,000 pathology reports from The Cancer Genome Atlas (TCGA) and Moffitt Cancer Center. Expert review confirmed high agreement in the TCGA dataset for behavior (100.0%), histology (98.5%), site (95.2%), and grade (95.6%), with lower performance for stage (87.6%) and laterality (84.8%). In the pathology reports from Moffitt (brain, breast, and lung), accuracy remained high across variables, with histology (95.6%), behavior (98.3%), and stage (92.4%), achieving strong agreement. However, certain challenges emerged, such as inconsistent mention of sentinel lymph node details or anatomical ambiguity in biopsy site interpretations. Statistical analyses revealed significant main effects of model type, variable, and organ system, as well as model × variable × organ interactions, emphasizing the role of clinical context in model performance. These results highlight the importance of stratified, multi-organ evaluation frameworks in LLM benchmarking for clinical applications. Textual justifications enhanced interpretability and enabled human reviewers to audit model outputs. Overall, this consensus-based approach demonstrates that locally deployed LLMs can provide a transparent, accurate, and auditable solution for integrating AI-driven data extraction into real-world pathology workflows, including cancer registry abstraction and synoptic reporting. 

Abstract BackgroundDiagnostic pathology depends on complex, structured reasoning to interpret clinical, histologic, and molecular data. Replicating this cognitive process algorithmically remains a significant challenge. As large language models (LLMs) gain traction in medicine, it is critical to determine whether they have clinical utility by providing reasoning in highly specialized domains such as pathology. MethodsWe evaluated the performance of four reasoning LLMs (OpenAI o1, OpenAI o3-mini, Gemini 2.0 Flash Thinking Experimental, and DeepSeek-R1 671B) on 15 board-style open-ended pathology questions. Responses were independently reviewed by 11 pathologists using a structured framework that assessed language quality (accuracy, relevance, coherence, depth, and conciseness) and seven diagnostic reasoning strategies. Scores were normalized and aggregated for analysis. We also evaluated inter-observer agreement to assess scoring consistency. Model comparisons were conducted using one-way ANOVA and Tukey’s Honestly Significant Difference (HSD) test. ResultsGemini and DeepSeek significantly outperformed OpenAI o1 and OpenAI o3-mini in overall reasoning quality (p < 0.05), particularly in analytical depth and coherence. While all models achieved comparable accuracy, only Gemini and DeepSeek consistently applied expert-like reasoning strategies, including algorithmic, inductive, and Bayesian approaches. Performance varied by reasoning type: models performed best in algorithmic and deductive reasoning and poorest in heuristic and pattern recognition. Inter-observer agreement was highest for Gemini (p < 0.05), indicating greater consistency and interpretability. Models with more in-depth reasoning (Gemini and DeepSeek) were generally less concise. ConclusionAdvanced LLMs such as Gemini and DeepSeek can approximate aspects of expert-level diagnostic reasoning in pathology, particularly in algorithmic and structured approaches. However, limitations persist in contextual reasoning, heuristic decision-making, and consistency across questions. Addressing these gaps, along with trade-offs between depth and conciseness, will be essential for the safe and effective integration of AI tools into clinical pathology workflows. 

Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.