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

Publication Type	Academic Article
Authors	Iranmahboub P, Dave P, Hung M, Pelt J, Ali H, Blum K, Ramaswamy A, Wahba B, Korniyenko A, Huang A, Angulo-Lozano J, Xu H, Suzman E, Posada Calderon L, Borregales L, Scherr D
Journal	Sci Rep
Volume	16
Issue	1
Date Published	06/10/2026
ISSN	2045-2322
Keywords	Natural Language Processing, Prostatectomy, Prostatic Neoplasms
Abstract	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.
DOI	10.1038/s41598-026-55613-7
PubMed ID	42270728
PubMed Central ID	PMC13494025