August 15, 2025Open Access

Evaluating the Efficacy of Large Language Models in Addressing Patient-Centric Inquiries in Multiple Cancers

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Discussion

Systematic review assesses reliability and accessibility of LLMs in answering cancer-related patient questions, suggesting improvement avenues.

Large language models show promise in providing reliable information, with accuracy averaging around 79.0%.
Accessibility scores were concerning, particularly actionability at a median of 40.0%, indicating a need for improvement.
The systematic review included thirty-six studies that used various measures to evaluate LLM responses in oncology contexts.
Training with physician involvement may enhance the performance of large language models in healthcare communication.

Borhani et al. (2025) studied this question.

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August 15, 2025Open Access

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Authors

Systematic review assesses reliability and accessibility of LLMs in answering cancer-related patient questions, suggesting improvement avenues.

Large language models show promise in providing reliable information, with accuracy averaging around 79.0%.
Accessibility scores were concerning, particularly actionability at a median of 40.0%, indicating a need for improvement.
The systematic review included thirty-six studies that used various measures to evaluate LLM responses in oncology contexts.
Training with physician involvement may enhance the performance of large language models in healthcare communication.

Borhani et al. (2025) studied this question.

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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