{
  "abstract": "Introduction Uncertainty around medical liability and accountability remains a barrier to the implementation of AI in endoscopy. To date, no malpractice cases have involved AI-enabled endoscopy. As systems become increasingly automated, responsibility becomes more complex, involving clinicians, hospitals and manufacturers. A better understanding of public perception around AI and liability will be critical for clinical adoption and the development of legal frameworks. This study examines how laypeople assign responsibility across stakeholders at different levels of AI automation in endoscopy.Methods An online survey was conducted via a dedicated research platform (Prolific). Adults > 18 years old from the USA and Europe were randomly presented with three AI endoscopy harm scenarios representing increasing levels of automation. Participants rated responsibility for four groups [doctor, hospital, AI manufacturer and a no-fault compensation scheme] using a 7-point Likert scale. Scenario 1 involved a computer aided quality (CAQ) tool reporting adequate mucosal visualisation followed by a missed colorectal cancer (CRC). Scenario 2 involved a computer aided diagnosis (CADx) system misclassifying an adenoma as a hyperplastic polyp, leaving an adenoma in situ, which later progressed to CRC. Scenario 3 described a capsule endoscopy tool with which physicians check only images flagged by the system as abnormal, failing to identify a significant gastrointestinal bleed.Results 502 respondents completed the survey (USA: 250; Europe: 252). Responsibility scores varied significantly by both AI automation level and stakeholder (p < 0.001). Doctors received the highest overall mean responsibility score of any stakeholder (5.15, p < 0.001). However, accountability patterns shifted significantly with increasing AI automation. Doctors’ mean responsibility declined steadily, reaching 3.32 at the highest automation level (p < 0.001). In contrast, hospitals and manufacturers showed the opposite trend. Their mean responsibility scores peaked at the highest automation level, at 5.73 and 5.83, respectively (p < 0.001). Ratings for the no-fault compensation scheme remained largely unchanged.Conclusion This is the first study to examine public perceptions of liability for AI-related harms in endoscopy. Participants perceived that increasing AI automation shifts responsibility away from clinicians towards hospitals and manufacturers, though clinicians were assigned high responsibility across all scenarios. As automation increases, hospitals and manufacturers should strengthen governance and monitoring to ensure safety and accountability. These findings underscore the need to preserve a human-in-the-loop role within AI-supported endoscopy and to establish structured liability frameworks to guide clinical practice.Abstract O28 Figure 1Effect of automation level on stakeholder responsibility ratings",
  "authors": [
    {
      "affiliations": [
        "Department of Surgery & Interventional Sciences, University College London, London, United Kingdom"
      ],
      "name": "Ahmed El-Sayed"
    },
    {
      "affiliations": [
        "Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway"
      ],
      "name": "Yuichi Mori"
    },
    {
      "affiliations": [
        "University of Illinois Urbana-Champaign, College of Law and European Union Center, Champaign, United States of America"
      ],
      "name": "Sara Gerke"
    },
    {
      "affiliations": [
        "Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, United States of America",
        "Harvard Medical School, Boston, United States of America"
      ],
      "name": "Tyler M Berzin"
    },
    {
      "affiliations": [
        "Department of Biomedical Sciences, Humanitas University, Milan, Italy",
        "IRCCS Humanitas Research Hospital Department of Gastroenterology, Milan, Italy"
      ],
      "name": "Cesare Hassan"
    },
    {
      "affiliations": [
        "Department of Surgery & Interventional Sciences, University College London, London, United Kingdom"
      ],
      "name": "Laurence B Lovat"
    },
    {
      "affiliations": [
        "Department of Surgery & Interventional Sciences, University College London, London, United Kingdom",
        "Gastrointestinal Services, University College Hospital London, London, United Kingdom"
      ],
      "name": "Omer F Ahmad"
    }
  ],
  "title": "O28 Public perspectives on liability for errors with AI-enabled gastrointestinal endoscopy",
  "uid": "e3b0bc58-c67d-519c-ad96-1103d473c9c8"
}