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<p>Machine translation supported by artificial intelligence (AI) may enhance linguistically-concordant care for patients speaking languages other than English. This assessment of free-text inpatient discharge instructions in Arabic, Armenian, Bengali, simplified Chinese, Somali, and Spanish compared linguist, clinician, and family caregiver evaluations of translations generated by (1) ChatGPT-4o, (2) professional linguists, and (3) human-in-the-loop (AI-generated, professional linguist post-edited). Likert scales (1–5; higher is better) evaluated linguistic and clinical characteristics of each translation. ChatGPT-4o exhibited variable performance relative to professional translations, with poorest ratings for digitally underrepresented languages (Armenian and Somali). Conversely, human-in-the-loop translations achieved comparable, often better, outcomes to professional translations for all languages, (e.g., Armenian mean overall quality: 3.9 [95% CI 3.7–4.2] vs. professional 3.6 [3.4–3.9], <i>p</i> = 0.01), were most frequently preferred (46.5% vs. 28.4%) and had shorter mean translation time (7.1 [5.4–8.8] vs. 16.8 [13.7–19.9] min, <i>p</i> &lt; 0.001). Human-in-the-loop strategies may enable safe, efficient, equitable machine translation application in clinical practice.</p>

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Evaluating human-in-the-loop strategies for artificial intelligence-enabled translation of patient discharge instructions: a multidisciplinary analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Ryan CL Brewster,
  • Gabriel Tse,
  • Angela L. Fan,
  • Marwa Elborki,
  • Maiah Newell,
  • Priscilla Gonzalez,
  • Amitra Hoq,
  • Crystal Chang,
  • Maksud Chowdhury,
  • Adiba Geeti,
  • Marlin Hana,
  • Hoda Hassan,
  • Osama Ibrahim,
  • Lucine Keseyan,
  • Qing Li,
  • Md Mamoon,
  • Maymona Nageye,
  • Arthur Ohannessian,
  • Ilan Rozen Eisenberg,
  • Mohammad Sallam,
  • Giordano Sosa Soto,
  • Christina Su,
  • Raffi Tachdjian,
  • Mondira Ray,
  • Hannah Lev,
  • Jonathan D. Hron,
  • Nate Shaar,
  • Nicholas Kuzma,
  • Alisa Khan

摘要

Machine translation supported by artificial intelligence (AI) may enhance linguistically-concordant care for patients speaking languages other than English. This assessment of free-text inpatient discharge instructions in Arabic, Armenian, Bengali, simplified Chinese, Somali, and Spanish compared linguist, clinician, and family caregiver evaluations of translations generated by (1) ChatGPT-4o, (2) professional linguists, and (3) human-in-the-loop (AI-generated, professional linguist post-edited). Likert scales (1–5; higher is better) evaluated linguistic and clinical characteristics of each translation. ChatGPT-4o exhibited variable performance relative to professional translations, with poorest ratings for digitally underrepresented languages (Armenian and Somali). Conversely, human-in-the-loop translations achieved comparable, often better, outcomes to professional translations for all languages, (e.g., Armenian mean overall quality: 3.9 [95% CI 3.7–4.2] vs. professional 3.6 [3.4–3.9], p = 0.01), were most frequently preferred (46.5% vs. 28.4%) and had shorter mean translation time (7.1 [5.4–8.8] vs. 16.8 [13.7–19.9] min, p < 0.001). Human-in-the-loop strategies may enable safe, efficient, equitable machine translation application in clinical practice.