ArticleJAMIA open2026
Enhancing the quality and trustworthiness of large language model-generated summaries of clinical oncology literature.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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13 authors.
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Abstract
Objectives: This study evaluated the quality and trustworthiness of large language model (LLM)-generated scientific and plain language summaries (PLS) from clinical oncology literature, focusing on faithfulness (absence of hallucinations), relevance, and readability. Materials and Methods: Ten LLM-generated scientific summaries and PLS from the INSIDE (artificial INtelligence to Support Informed DEcision making) prostate cancer dataset. For comparison, expert-written PLS from the BioLaySumm dataset were used. A panel of 5 LLMs and 3 human experts verified faithfulness. Verification was performed on original facts and facts modified with varying levels of error (subtle, moderate, contradictory). Readability was assessed using Flesch-Kincaid Reading Ease (FRE) scores. Results: Fact verification against the summaries was ∼100%, confirming accurate fact extraction. LLM panel vs human panel agreement was substantial (kappa 0.67), outperforming agreement among the interhuman (0.43 [95% CI, 0.34-0.52]) and inter-LLM (0.40 [0.38-0.42]) panels. Large language model scientific summaries showed high faithfulness (88.9% [88.0-89.8]) and low hallucinations (9.6% [6.5-12.7]) compared to human-written PLS (61.6% [60.1-63.1] faithfulness; 40.6% [37.8- 43.4] hallucinations). The LLMs detected errors sensitively with scores decreasing as fact modifications became more severe. Finally, LLM-generated PLS were more readable than human-written versions (FRE 42.3 [interquartile range, IQR 35.27-49.41] vs 28.8 [IQR 21.02-36.18]). Discussion: A panel of LLMs reliably assessed the faithfulness of scientific summaries to their original source and thus can help increase reliability for clinical use. The lower faithfulness in human-written PLS likely reflects extrinsic hallucinations added for context. Conclusion: The study demonstrates a novel approach to automatically assess the quality and trustworthiness of LLM-generated scientific and PLS via faithfulness, relevance, and readability.
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