JAMIA open · 2026
Enhancing the quality and trustworthiness of large language model-generated summaries of clinical oncology literature.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Stenzl Arnulf, Rogers Eamonn, Ananiadou Sophia, Wang Yanshan, Armstrong Andrew J, Sboner Andrea, Cacciamani Giovanni, Schijvenaars Bob J A, Ahmed Kausar Riaz, Thomsen Hanna, Wiemken Timothy, Campello Antonio, Sternberg Cora N
Аннотация
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.
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.