Natural language processing models for patient-centered summaries of prostatectomy pathology reports

Many patients now view their radical prostatectomy (RP) pathology before provider discussion, increasing anxiety and administrative burden. With expanding utilization of AI-assisted medical workflows, it is important to implement strategies to improve the interpretation of patient-facing information involving complex medical language. We compare a rules-based natural language processing (NLP) model and a large language model (LLM) using zero-shot prompting to identify the optimal framework for developing accurate, patient-facing RP pathology summaries. Models were assessed for accuracy in extracting key pathology features, calculating recurrence-free probabilities at varying intervals, and providing clinical recommendations from pathology reports at a single institution. Error-free summaries were generated in 92% of rules-based NLP and 97% of LLM reports (p = 0.18). In an external test set of differently formatted RP pathology reports from a separate institution, the LLM maintained high accuracy without additional training, while the rules-based NLP model achieved similarly high accuracy after minimal refinement. These findings suggest that both approaches can effectively support patient-facing pathology summaries, allowing practices and hospital systems to adopt the framework best suited to their technical resources, financial considerations, governance infrastructure, and institutional priorities.