Paediatric rare diseases: Can large language models assist off-label prescribing?

ABSTRACT:

Aims

To evaluate the effectiveness and reliability of large language models (LLMs) in retrieving and synthesizing biomedical information to support off-label drug prescribing in paediatric rare diseases, and to compare their performance with human-authored references in terms of scientific rationale, adverse events and drug interactions.

Methods

The study reviewed 20 cases of off-label prescriptions in rare paediatric diseases using 4 LLMs (i.e., GPT-4o, Sophos-2, Claude-3, Scopus AI). The queries addressed focused on scientific rationale, adverse events and drug interactions. The performance measures encompassed sensitivity, precision, accuracy, F1-score, response quality and reference quality. A Global Performance Score integrated all measures.

Results

After evaluat

SUBMITTER: Flamigni A 

PROVIDER: S-EPMC12569560 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature