Medical MalpracticeNCT07869303
The Impact of AI-Assisted Diagnosis and Treatment on Physicians' Potential Legal Liability: An Expert Perspective
A study testing General AI With a Standard-of-Care Recommendation, General AI With a Non-Standard-of-Care Recommendation and Expert AI With a Standard-of-Care Recommendation in people with Medical Malpractice and Artificial Intelligence in Medicine, run by Xie Xiaoyun.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Medical Malpractice, Artificial Intelligence in Medicine, Clinical Decision-Making
- What is being tested?
- General AI With a Standard-of-Care Recommendation, General AI With a Non-Standard-of-Care Recommendation and Expert AI With a Standard-of-Care Recommendation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 440 participants
- Where
- No sites listed yet
- Study dates
- 30 Sep 2026to 20 Aug 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
- Able to read and understand the clinical vignettes and complete the online survey independently.
Who cannot join
- Physicians who are not qualified to participate in medical negligence assessments.
- Forensic medical experts who are not qualified to conduct medical negligence assessments independently or are not affiliated with an officially recognized forensic assessment institution.
- Unable to understand or complete the vignette-based online survey.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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.
The main questions it aims to answer are:
Read more
Does physician acceptance or rejection of AI recommendations, compared with non-AI use, influence the likelihood of being assessed as negligent?
Researchers 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.
Participants will:
Complete 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.
Read the detailed description
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/patient-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.
- Being tested
- General AI With a Standard-of-Care Recommendation, General AI With a Non-Standard-of-Care Recommendation, Expert AI With a Standard-of-Care Recommendation, Expert AI With a Non-Standard-of-Care Recommendation
- Keywords
- Artificial intelligence, clinical decision support, medical negligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
General AI With a Standard-of-Care RecommendationOther
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.
General AI With a Non-Standard-of-Care RecommendationOther
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.
Expert AI With a Standard-of-Care RecommendationOther
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.
Expert AI With a Non-Standard-of-Care RecommendationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| No AI Assistance (Control Group) | No intervention | No AI Assistance (Control Group) |
| Intervention Group 1 | Active comparator | General AI with a Standard-of-Care Recommendation Receives: Other: General AI With a Standard-of-Care Recommendation |
| Intervention Group 2 | Active comparator | General AI with a Non-Standard-of-Care Recommendation Receives: Other: General AI With a Non-Standard-of-Care Recommendation |
| Intervention Group 3 | Active comparator | Expert AI with a Standard-of-Care Recommendation Receives: Other: Expert AI With a Standard-of-Care Recommendation |
| Intervention Group 4 | Active comparator | Expert AI with a Non-Standard-of-Care Recommendation Receives: Other: Expert AI With a Non-Standard-of-Care Recommendation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Immediately after each clinical vignette during the single online survey session.
Secondary outcomes (6)
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.
Measured: Immediately after each clinical vignette during the single online survey session.
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.
Measured: Immediately after each clinical vignette during the single online survey session.
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%.
Measured: Immediately after each clinical vignette during the single online survey session.
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.
Measured: Immediately after each clinical vignette during the single online survey session.
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.
Measured: Immediately after completion of all four clinical vignettes during the single online survey session.
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.
Measured: Immediately after completion of all four clinical vignettes during the single online survey session.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Oct 2026
- Study start30 Sep 2026 (estimated)
- Primary completion15 Jun 2027 (estimated)
- Study completion20 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Medical MalpracticeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 440 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xie Xiaoyun
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Xie Xiaoyun, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07869303
- Sponsor's protocol ID
- [2026] Thesis Review No.(S102）
- Status last verified
- Sep 2026
- First submitted
- 16 Sep 2026
- Last updated
- 9 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: According to the research protocol, individual participant data from this study cannot be deposited in a public data repository but can be made available for research purposes upon request. Researchers can request the de-identified data by submitting a data use agreement (DUA) to the Medical Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. The DUA must state that the data will be used for research purposes only and that no attempt will be made to re-identify participants. Upon approval of the request, the data will be made available. The contact information is as follows: tongjilunli@vip.163.com (email); +86-27-83691785 (fax); Office 12, 16th Floor, Teaching Building 2, School of Basic Medicine, Tongji Medical College, No. 13 Hangkong Road, Wuhan, Hubei Province, China (address).
Study at a glance
From the registry record, last updated by the sponsor on 9 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Impact of AI-Assisted Diagnosis and Treatment on Physicians' Potential Legal Liability: An Expert Perspective
- ClinicalTrials.gov ID
- NCT07869303
- Acronym / sponsor ID
- [2026] Thesis Review No.(S102）
- Keywords
- Artificial intelligence, clinical decision support, medical negligence
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 440 participants (estimated)
- Lead sponsor
- Xie XiaoyunUniversity, hospital or other
- Study start
- 30 Sep 2026 (estimated)
- Primary completion
- 15 Jun 2027 (estimated)
- Study completion
- 20 Aug 2027 (estimated)
- First posted
- 9 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
The main questions it aims to answer are:
Does physician acceptance or rejection of AI recommendations, compared with non-AI use, influence the likelihood of being assessed as negligent?
Researchers 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.
Participants will:
Complete 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.
Detailed description
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/patient-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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, investigator)
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| No AI Assistance (Control Group) | No intervention | –No AI Assistance (Control Group) |
| Intervention Group 1 | Active comparator | Other: General AI With a Standard-of-Care RecommendationGeneral AI with a Standard-of-Care Recommendation |
| Intervention Group 2 | Active comparator | Other: General AI With a Non-Standard-of-Care RecommendationGeneral AI with a Non-Standard-of-Care Recommendation |
| Intervention Group 3 | Active comparator | Other: Expert AI With a Standard-of-Care RecommendationExpert AI with a Standard-of-Care Recommendation |
| Intervention Group 4 | Active comparator | Other: Expert AI With a Non-Standard-of-Care RecommendationExpert AI with a Non-Standard-of-Care Recommendation |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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.
- Able to read and understand the clinical vignettes and complete the online survey independently.
Key exclusion criteria (3 listed)
- Physicians who are not qualified to participate in medical negligence assessments.
- Forensic medical experts who are not qualified to conduct medical negligence assessments independently or are not affiliated with an officially recognized forensic assessment institution.
- Unable to understand or complete the vignette-based online survey.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| General AI With a Standard-of-Care RecommendationParticipants 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. | Other | Intervention Group 1 |
| General AI With a Non-Standard-of-Care RecommendationParticipants 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. | Other | Intervention Group 2 |
| Expert AI With a Standard-of-Care RecommendationParticipants 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. | Other | Intervention Group 3 |
| Expert AI With a Non-Standard-of-Care RecommendationParticipants 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. | Other | Intervention Group 4 |
Primary outcome
Physician Negligence Assessment
Time frame: Immediately after each clinical vignette during the single online survey session.
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).
6 secondary outcomes
Reasonableness of the Physician's Decision
Time frame: Immediately after each clinical vignette during the single online survey session.
Magnitude of Physician Fault
Time frame: Immediately after each clinical vignette during the single online survey session.
Allocation of Responsibility
Time frame: Immediately after each clinical vignette during the single online survey session.
Confidence in the Assessment
Time frame: Immediately after each clinical vignette during the single online survey session.
Intention to Use AI in Future Clinical Practice
Time frame: Immediately after completion of all four clinical vignettes during the single online survey session.
Confidence in Using AI in Future Clinical Practice
Time frame: Immediately after completion of all four clinical vignettes during the single online survey session.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Oct 2026.
- Individual participant dataSharing planned: Yes. According to the research protocol, individual participant data from this study cannot be deposited in a public data repository but can be made available for research purposes upon request. Researchers can request the de-identified data by submitting a data use agreement (DUA) to the Medical Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. The DUA must state that the data will be used for research purposes only and that no attempt will be made to re-identify participants. Upon approval of the request, the data will be made available. The contact information is as follows: tongjilunli@vip.163.com (email); +86-27-83691785 (fax); Office 12, 16th Floor, Teaching Building 2, School of Basic Medicine, Tongji Medical College, No. 13 Hangkong Road, Wuhan, Hubei Province, China (address).
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing General AI With a Standard-of-Care Recommendation, General AI With a Non-Standard-of-Care Recommendation and Expert AI With a Standard-of-Care Recommendation in people with Medical Malpractice and Artificial Intelligence in Medicine, run by Xie Xiaoyun.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 9 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xiaoyun Xie, Master (+86 18061291721, xxy996269@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Physician Negligence Assessment (time frame: Immediately after each clinical vignette during the single online survey session.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Oct 2026.