[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100659691":3},{"organization":4,"outcomesModule":7,"designInfo":34,"detailedDescription":44,"studyPopulation":33,"armGroups":45,"interventions":70,"overallOfficials":33,"centralContacts":87,"locations":33,"responsibleParty":93,"collaborators":33,"id":97,"slug":98,"hasResults":99,"nctId":100,"briefTitle":101,"officialTitle":101,"acronym":33,"eligibilityCriteria":102,"healthyVolunteers":103,"sex":104,"minAge":105,"maxAge":33,"enrollmentInfo":106,"targetDuration":33,"studyType":109,"phases":110,"briefSummary":112,"conditions":113,"keywords":117,"overallStatus":121,"whyStopped":33,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":126,"completionDateStruct":128,"leadSponsor":130,"locationsCount":33},{"fullName":5,"class":6},"Huazhong University of Science and Technology","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":33},[9],{"measure":10,"description":11,"timeFrame":12},"Physician Negligence Assessment","Participants will assess whether the physician fulfilled the duty of care in each clinical vignette. Responses are recorded as \"Yes\" (no negligence) or \"No\" (negligence).","Immediately after each clinical vignette during the single online survey session.",[14,17,20,23,26,30],{"measure":15,"description":16,"timeFrame":12},"Reasonableness of the Physician's Decision","Participants will rate the extent to which a reasonable physician would make the same decision under similar circumstances. Scores range from 1 (definitely would not) to 7 (definitely would), with higher scores indicating greater perceived reasonableness.",{"measure":18,"description":19,"timeFrame":12},"Magnitude of Physician Fault","Participants will rate the magnitude of the physician's fault using a visual analogue scale ranging from 0 (completely faultless) to 100 (completely at fault). Higher scores indicate greater perceived fault.",{"measure":21,"description":22,"timeFrame":12},"Allocation of Responsibility","Participants will allocate responsibility for the adverse outcome among the physician, healthcare institution, AI developer and others(e.g. patient, disease factors). The percentages assigned to the three parties must total 100%.",{"measure":24,"description":25,"timeFrame":12},"Confidence in the Assessment","Participants will rate their confidence in their assessments on a 7-point Likert scale ranging from 1 (very unconfident) to 7 (very confident). Higher scores indicate greater confidence.",{"measure":27,"description":28,"timeFrame":29},"Intention to Use AI in Future Clinical Practice","Participants will rate their intention to use AI to support future clinical decision-making on a 7-point Likert scale ranging from 1 (very unwilling) to 7 (very willing). Higher scores indicate greater intention to use AI.","Immediately after completion of all four clinical vignettes during the single online survey session.",{"measure":31,"description":32,"timeFrame":29},"Confidence in Using AI in Future Clinical Practice","Participants will rate their confidence in using AI to support future clinical decision-making on a 7-point Likert scale ranging from 1 (not confident at all) to 7 (completely confident). Higher scores indicate greater confidence in using AI.",null,{"allocation":35,"interventionModel":36,"interventionModelDescription":33,"primaryPurpose":37,"observationalModel":33,"timePerspective":33,"maskingInfo":38},"RANDOMIZED","PARALLEL","HEALTH_SERVICES_RESEARCH",{"masking":39,"maskingDescription":40,"whoMasked":41},"DOUBLE","Due to the nature of intervention, participants will be aware of their groups (control vs intervention), but blinded to the specific intervention group if assigned to one. All participants will initiate the study by accessing an identical, standardized link to the study website. Upon entering the website and provide consent, the built-in, automated randomization algorithm will assign participants to one of the five groups (ratio: 1:1:1:1:1).",[42,43],"PARTICIPANT","INVESTIGATOR","Using a randomized vignette-based experiment, this study will empirically examine how contextual factors identified through a literature review and expert consultation influence assessments of physician negligence. The vignettes will vary factors such as the physician's clinical competence and use of AI and will be presented in randomized order to physicians potentially qualified to participate in medicolegal assessments. Participants will then determine whether the physician's final clinical decision in each vignette constitutes negligence. Participants will be randomly assigned using simple randomization to one no-AI control group or one of four AI intervention groups. Based on the contextual factors identified, the study will include as many factors as feasible within the available sample size, with clearly defined factors examined in the primary or exploratory analyses. Different vignettes will be constructed using combinations of these factors. After assessing whether the physician in each vignette was negligent, participants will also complete measures concerning the extent to which the physician fulfilled the duty of care, their confidence in the assessment, and the allocation of responsibility among the AI developer, healthcare institution, physician, and others (e.g., disease\u002Fpatient-related factors). These data will be used to identify the circumstances under which accepting or rejecting an AI recommendation is more likely to be considered negligent and to examine medicolegal experts' current attitudes toward the role of medical AI in assessments of medical malpractice.",[46,49,55,60,65],{"label":47,"type":48,"description":47,"interventionNames":33},"No AI Assistance (Control Group)","NO_INTERVENTION",{"label":50,"type":51,"description":52,"interventionNames":53},"Intervention Group 1","ACTIVE_COMPARATOR","General AI with a Standard-of-Care Recommendation",[54],"Other: General AI With a Standard-of-Care Recommendation",{"label":56,"type":51,"description":57,"interventionNames":58},"Intervention Group 2","General AI with a Non-Standard-of-Care Recommendation",[59],"Other: General AI With a Non-Standard-of-Care Recommendation",{"label":61,"type":51,"description":62,"interventionNames":63},"Intervention Group 3","Expert AI with a Standard-of-Care Recommendation",[64],"Other: Expert AI With a Standard-of-Care Recommendation",{"label":66,"type":51,"description":67,"interventionNames":68},"Intervention Group 4","Expert AI with a Non-Standard-of-Care Recommendation",[69],"Other: Expert AI With a Non-Standard-of-Care Recommendation",[71,75,79,83],{"type":6,"name":72,"description":73,"armGroupLabels":74,"otherNames":33},"General AI With a Standard-of-Care Recommendation","Participants will evaluate four clinical vignettes involving a general AI system whose historical performance is comparable to that of a township hospital attending physician. The AI provides a recommendation consistent with the standard of care. Physician capability and acceptance or rejection of the AI recommendation vary across the vignettes.",[50],{"type":6,"name":76,"description":77,"armGroupLabels":78,"otherNames":33},"General AI With a Non-Standard-of-Care Recommendation","Participants will evaluate four clinical vignettes involving a general AI system whose historical performance is comparable to that of a township hospital attending physician. The AI provides a recommendation inconsistent with the standard of care. Physician capability and acceptance or rejection of the AI recommendation vary across the vignettes.",[56],{"type":6,"name":80,"description":81,"armGroupLabels":82,"otherNames":33},"Expert AI With a Standard-of-Care Recommendation","Participants will evaluate four clinical vignettes involving an expert AI system whose historical performance is comparable to that of a tertiary hospital chief physician. The AI provides a recommendation consistent with the standard of care. Physician capability and acceptance or rejection of the AI recommendation vary across the vignettes.",[61],{"type":6,"name":84,"description":85,"armGroupLabels":86,"otherNames":33},"Expert AI With a Non-Standard-of-Care Recommendation","Participants will evaluate four clinical vignettes involving an expert AI system whose historical performance is comparable to that of a tertiary hospital chief physician. The AI provides a recommendation inconsistent with the standard of care. Physician capability and acceptance or rejection of the AI recommendation vary across the vignettes.",[66],[88],{"name":89,"role":90,"phone":91,"phoneExt":33,"email":92},"Xiaoyun Xie, Master","CONTACT","+86 18061291721","xxy996269@163.com",{"type":94,"investigatorFullName":95,"investigatorTitle":96,"investigatorAffiliation":5,"oldNameTitle":33,"oldOrganization":33},"SPONSOR_INVESTIGATOR","Xie Xiaoyun","Head of Otolaryngology","100659691","the-impact-of-ai-assisted-diagnosis-and-treatment-on-physicians-potential-legal-liability-an-expert-perspective-100659691",false,"NCT07869303","The Impact of AI-Assisted Diagnosis and Treatment on Physicians' Potential Legal Liability: An Expert Perspective","Inclusion Criteria:\n\n1. Physicians qualified to participate in medical negligence assessments, generally holding the title of associate chief physician or above and having relevant clinical experience; or forensic medical experts affiliated with an officially recognized forensic assessment institution and qualified to conduct medical negligence assessments independently.\n2. Able to read and understand the clinical vignettes and complete the online survey independently.\n\nExclusion Criteria:\n\n1. Physicians who are not qualified to participate in medical negligence assessments.\n2. Forensic medical experts who are not qualified to conduct medical negligence assessments independently or are not affiliated with an officially recognized forensic assessment institution.\n3. Unable to understand or complete the vignette-based online survey.",true,"ALL","18 Years",{"count":107,"type":108},440,"ESTIMATED","INTERVENTIONAL",[111],"NA","The goal of this randomized vignette-based experiment is to investigate how AI-related contextual factors influence expert assessment of physician negligence in AI-assisted diagnosis and treatment scenarios among experts in medical malpractice assessment.\n\nThe main questions it aims to answer are:\n\nDoes physician acceptance or rejection of AI recommendations, compared with non-AI use, influence the likelihood of being assessed as negligent?\n\nResearchers will compare a no-AI assistance control group with four AI-assistance groups under different AI use conditions to examine how AI involvement and contextual factors affect assessments of physician negligence.\n\nParticipants will:\n\nComplete a web-based questionnaire after eligibility confirmation. Evaluate a series of clinical vignettes describing adverse patient outcomes following physician decision-making under different AI-use conditions.",[114,115,116],"Medical Malpractice","Artificial Intelligence in Medicine","Clinical Decision-Making",[118,119,120],"Artificial intelligence","clinical decision support","medical negligence","NOT_YET_RECRUITING","2026-10-07",{"date":124,"type":125},"2026-10-09","ACTUAL",{"date":127,"type":108},"2026-09-30",{"date":129,"type":108},"2027-08-20",{"name":95,"class":6}]