{
  "abstract": "The deployment of artificial intelligence (AI) translation tools in healthcare is accelerating rapidly, yet regulatory frameworks lag dangerously behind clinical practice. Recent data reveal that 57% of US physicians are already using or planning to adopt AI translation services within the next year. This creates a critical policy vacuum where clinicians deploy tools with variable performance across languages, risking patient safety and deepening health inequities. We examine the fractured regulatory landscape, document performance disparities between well-resourced and digitally under-represented languages, and argue for an urgent, evidence-informed policy framework centred on patient comprehension rather than linguistic accuracy.We delineate a risk-stratified validation approach comprising two distinct tracks: a ‘Streamlined Pathway’ for tool-language combinations with robust existing evidence (eg, Spanish) and a ‘Standard Pathway’ requiring independent, prospective validation for digitally under-represented languages (eg, Haitian Creole). To ensure accountability, we propose establishing oversight bodies within the U.S. Department of Health and Human Services (HHS) or the Food and Drug Administration (FDA) to mandate pre-deployment validation and post-market monitoring. Without such action, AI translation risks creating a two-tier system where the 25.7 million Americans with non-English language preferences receive dramatically different care quality based solely on the language they speak.",
  "authors": [
    {
      "affiliations": [
        "School of Medicine, Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Chukwuebuka Anyaegbuna"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Natasha Steele"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "April Shichu Liang"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Stephen P Ma"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Ivan Lopez"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Nymisha Chilukuri"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Kavita Patel"
    },
    {
      "affiliations": [
        "Stanford University School of Medicine, Stanford, California, USA"
      ],
      "name": "Kevin Schulman"
    },
    {
      "affiliations": [
        "Biomedical Informatics, Stanford University, Palo Alto, California, USA"
      ],
      "name": "Jonathan H Chen"
    }
  ],
  "title": "Artificial intelligence translation in healthcare: an urgent call for evidence-informed policy frameworks",
  "uid": "bb3f7514-64e4-50c9-a48e-9db1a58e1078"
}