Large Language Models for Self-Care: Accuracy and Safety Benchmark

This paper, titled "The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy," presents a study investigating the effectiveness of large language models in answering patients' self-care inquiries about medications and supplements. The authors analyzed responses from six major language models for accuracy, language-independence, context-sensitivity, and reproducibility using a new reference set of questions and scoring matrix. The results show that while these models can generally provide accurate and relevant health information, there is substantial variability in their responses, influenced by language, question structure, user context, and time. The study found that GPT 4.0 scored highest on average, while others had varied scores. Due to their high-quality output, the potential of large language models in self-care applications is significant, but safeguards must be implemented to minimize the risk of misinformation.


REFERENCE:


De Busser B, Roth L, De Loof H. The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy.. International journal of clinical pharmacy. 2024 Dec 7 DOI: 10.1007/s11096-024-01839-2 PMID: 39644377. https://doi.org/10.1007/s11096-024-01...


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