Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
This addresses the problem of suboptimal management for patients with rare gynecological tumors by enabling biology-based personalized care, though it is incremental as it builds on existing digital twin and LLM concepts.
This study tackled the challenge of managing rare gynecological tumors by using large language models to create digital twins that integrate clinical and literature data, identifying personalized treatment options potentially missed by traditional methods in a proof-of-concept with 21 cases and 655 publications.
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs. Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes.