How different ways of assigning responsibility affect doctors when using artificial intelligence to make diagnoses
| ISRCTN | ISRCTN16943519 |
|---|---|
| DOI | https://doi.org/10.1186/ISRCTN16943519 |
| Sponsor | School of Business, Sun Yat-sen University |
| Funder | Investigator initiated and funded |
- Submission date
- 22/04/2026
- Registration date
- 20/06/2026
- Last edited
- 19/06/2026
- Recruitment status
- No longer recruiting
- Overall study status
- Completed
- Condition category
- Other
Plain English summary of protocol
Background and study aims
Doctors are starting to use artificial intelligence, often called AI, to help make medical diagnoses. Even when AI is used, doctors still have to decide how much responsibility they personally feel for the final decision. This study looks at whether different ways of assigning responsibility between doctors and AI change how accurate doctors are and how well they judge their own confidence. The aim is to find out which approach may lead to safer and higher quality diagnoses when AI is used.
Who can participate?
The study is open to hospital doctors who are currently practising or have recent clinical experience. Participants must be aged 18 years or older, able to read and understand the study materials, and able to complete an online study on their own.
What does the study involve?
Participants take part in an online study session. They are asked to review a series of short patient case descriptions, known as clinical vignettes, and make diagnostic decisions with the help of an AI tool. Doctors are randomly assigned to one of four groups, each given different information about who is responsible for the final diagnosis. After each case, participants also rate how confident they are in their decision. The study takes place in a single session and does not involve real patients.
What are the possible benefits and risks of participating?
There are no direct medical benefits to participants. However, the study may help improve how AI is safely used in healthcare in the future. The risks are minimal and mainly involve the time spent completing the study and possible mild frustration when working through the tasks. No clinical decisions about real patients are involved.
Where is the study run from?
The study is run by researchers at the School of Business, Sun Yat-sen University in Guangzhou, China, and is conducted online.
When is the study starting and how long is it expected to run for?
December 2025 to January 2026.
Who is funding the study?
Investigator initiated and funded.
Who is the main contact?
Miss Tianya Liu at the School of Business, Sun Yat-sen University, liuty73@mail2.sysu.edu.cn
Contact information
Public, Scientific, Principal investigator
School of Business, Sun Yat-sen University
Guangzhou
519000
China
 ORCID ID |
0009-0009-9365-2393 |
|---|---|
| Phone | +86 15702996366 |
| liuty73@mail2.sysu.edu.cn |
Study information
| Primary study design | Interventional |
|---|---|
| Allocation | Randomized controlled trial |
| Masking | Open (masking not used) |
| Control | Active |
| Assignment | Parallel |
| Purpose | Diagnostic |
| Scientific title | Impact of responsibility allocation structures on diagnostic quality in AI-assisted diagnosis: an individually randomised four-arm parallel-group controlled experiment |
| Study objectives | This study aims to examine how different responsibility allocation structures between doctors and artificial intelligence affect diagnostic quality in AI-assisted diagnosis. Specifically, it compares the effects of dynamic responsibility, full responsibility, shared responsibility, and a control condition on doctors' diagnostic accuracy and confidence calibration during clinical vignette-based diagnostic tasks. |
| Ethics approval(s) |
Approved 27/12/2025, Medical Ethics Committee of the School of Business, Sun Yat-sen University (School of Business, South Campus, Guangzhou Campus, Sun Yat-sen University, 135 Xingang West Road, Guangzhou, 510275, China; +86 020-31061569; glxy@mail.sysu.edu.cn), ref: BS20251227 |
| Health condition(s) or problem(s) studied | Diagnostic decision-making quality in AI-assisted diagnosis, with a focus on how different responsibility allocation structures affect doctors’ diagnostic accuracy and confidence calibration. |
| Intervention | Participants are practising hospital doctors randomly assigned to one of four parallel groups: dynamic responsibility, full responsibility, shared responsibility, or control. All participants complete the same set of AI-assisted clinical vignette diagnostic tasks using the same artificial intelligence tool. The intervention differs only in the responsibility framing provided to participants. In the dynamic responsibility group, responsibility cues are embedded at key decision points where the doctor's judgement diverges from the artificial intelligence recommendation, prompting active verification and reconsideration before a final decision is made. In the full responsibility group, participants are informed that they bear sole responsibility for the final diagnostic decision and its consequences. In the shared responsibility group, participants are informed that responsibility is shared equally between the doctor and the artificial intelligence system. In the control group, no specific responsibility framing is provided. The study uses an individually randomised, four-arm, parallel-group controlled experimental design. The main purpose is to assess whether responsibility allocation functions as a behavioural intervention that changes diagnostic performance in artificial intelligence-assisted decision-making. The study involved a single online experimental session. The total duration of the intervention was approximately 10 minutes for participants in all study arms. Participants completed the clinical decision-making tasks and the post-task questionnaire during the same session. There was no subsequent follow-up period. Participants were randomly assigned in a 1:1:1:1 ratio to one of four study arms: the dynamic responsibility allocation group, the full human responsibility group, the equal responsibility group, or the control group. Randomisation was implemented automatically through the Credamo online research platform using a computer-generated random allocation procedure. Neither the participants nor the researchers responsible for recruitment could manually select or alter the group allocation. |
| Intervention type | Behavioural |
| Primary outcome measure(s) |
|
| Key secondary outcome measure(s) | |
| Completion date | 31/01/2026 |
Eligibility
| Participant type(s) | |
|---|---|
| Age group | Mixed |
| Lower age limit | 18 Years |
| Upper age limit | 65 Years |
| Sex | All |
| Target sample size at registration | 105 |
| Total final enrolment | 96 |
| Key inclusion criteria | 1. Verified hospital doctors with current or recent clinical practice experience 2. Aged 18 years or above 3. Able to read and understand the study materials and provide informed consent 4. Able to complete the online experimental tasks independently 5. Willing to participate in an AI-assisted diagnostic decision-making study |
| Key exclusion criteria | 1. Individuals who are not licensed hospital doctors or practising physicians 2. Individuals younger than 18 years of age 3. Inability to read or understand the study materials or provide informed consent 4. Inability to complete the online experimental tasks independently 5. Previous participation in the same experiment 6. Failure to complete the assigned diagnostic vignette tasks or provision of unusable data |
| Date of first enrolment | 01/12/2025 |
| Date of final enrolment | 30/01/2026 |
Locations
Countries of recruitment
- China
Study participating centres
Results and Publications
| Individual participant data (IPD) Intention to share | No |
|---|
Study outputs
| Output type | Details | Date created | Date added | Peer reviewed? | Patient-facing? |
|---|---|---|---|---|---|
| Protocol file | 22/04/2026 | No | No |
Additional files
- 49404 Protocol.pdf
- Protocol file
Editorial Notes
19/06/2026: Trial's existence confirmed by Medical Ethics Committee of the School of Business, Sun Yat-sen University.
