Artificial Intelligence (AI) in DiagnosisDISCORD-DxNCT07844889
Training Diagnostic Specialists to Use Artificial Intelligence Safely: The DISCORD-Dx Study
A study testing DISCORD-Dx Training and Conventional AI-Literacy Training in people with Artificial Intelligence (AI) in Diagnosis and Decision Making, run by Sara Reza at 1 site in Pakistan.
Trial summary
This randomized study evaluated whether a structured reasoning strategy called DISCORD-Dx could help specialist doctors use artificial intelligence (AI) recommendations more safely during diagnostic decision-making. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence (AI) in Diagnosis, Decision Making
- What is being tested?
- DISCORD-Dx Training and Conventional AI-Literacy Training
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 46 participants
- Where
- 1 sitePakistan
- Study dates
- 6 Jun 2026to 2 Sep 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Practicing consultant specialist with current independent diagnostic responsibility.
- Specialty in chemical pathology, hematology, microbiology, histopathology, or diagnostic radiology.
Who cannot join
- Trainee status.
- Absence of independent reporting responsibility.
- Direct involvement in constructing or validating the participant's specialty case bank.
- Prior access to assessment cases or scoring keys.
- Inability to complete the digital assessment.
Trial locations and contacts
1 site in 1 country.
Pakistan1 site
Quaid-e-Azam Medical College
Bahawalpur, Punjab Province, 63100
About this study
Official title: DISCORD-Dx as a Cognitive Strategy for Calibrated Reliance on AI in Diagnosis
This randomized study evaluated whether a structured reasoning strategy called DISCORD-Dx could help specialist doctors use artificial intelligence (AI) recommendations more safely during diagnostic decision-making. The strategy was designed to help specialists benefit from correct AI advice while resisting plausible but incorrect AI recommendations.
Forty-six consultant specialists in pathology and diagnostic radiology from two hospitals in Bahawalpur, Pakistan, were randomly assigned to receive either DISCORD-Dx training or time-matched conventional AI-literacy training. During assessment, participants first recorded and locked their own diagnosis before seeing a standardized AI recommendation. They then reviewed the AI advice and entered a final diagnosis. The AI recommendations included both correct recommendations and deliberately generated plausible errors that had been independently reviewed by specialists.
The main outcome was appropriate reliance on AI, defined as following correct AI advice or resisting erroneous AI advice immediately after training. Other outcomes included harmful switching from a correct diagnosis to an incorrect diagnosis after erroneous AI advice, over-reliance, under-reliance, final diagnostic accuracy, decision time, and appropriate reliance at 8 weeks. No participant interacted with a live AI system, and study responses did not affect patient care.
Read the detailed description
DISCORD-Dx is a seven-step cognitive strategy designed to support calibrated reliance on AI during diagnostic decision-making: Diagnose independently; Inspect the AI recommendation; Substantiate the evidence; Classify the disagreement; Override, modify, or accept; Review the outcome; and Demonstrate transfer.
Participants were consultant specialists with independent diagnostic responsibility in chemical pathology, hematology, microbiology, histopathology, or diagnostic radiology. Before randomization, participants completed four specialty-specific cases without AI. They were then randomized 1:1 to DISCORD-Dx training or an active control consisting of conventional AI-literacy training. Both groups received the same 20-minute orientation on AI limitations, confident error, verification, privacy, bias, and professional oversight, followed by a 40-minute group-specific training module.
Immediately after training, participants completed eight unseen specialty-specific cases, four containing correct AI recommendations and four containing deliberately erroneous but plausible AI recommendations. For each case, participants recorded an independent diagnosis before viewing the AI recommendation and then entered their final decision after reviewing the AI advice. At 8 weeks, participants completed six additional cases without refresher training. All AI recommendations were frozen and independently expert-reviewed before participant exposure, and participants did not interact with a live AI system.
- Being tested
- DISCORD-Dx Training, Conventional AI-Literacy Training
- Keywords
- Artificial Intelligence, Calibrated Reliance, Automation Bias, Human-AI Interaction, Diagnostic Decision-Making
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DISCORD-Dx TrainingBehavioural
A structured 40-minute cognitive training intervention delivered after a common 20-minute AI-safety orientation. The DISCORD-Dx module taught seven steps: Diagnose independently; Inspect AI advice; Substantiate evidence; Classify disagreement; Override, modify, or accept; Review outcome; and Demonstrate transfer. Guided practice cases were included, and delivery was standardized using locked scripts and fidelity checklists.
Conventional AI-Literacy TrainingBehavioural
A 40-minute active-control training intervention delivered after the same 20-minute AI-safety orientation. It reinforced conventional principles of AI literacy through matched case discussion but did not include the DISCORD-Dx mnemonic, disagreement taxonomy, or the explicit accept/modify/override sequence. Contact time, facilitator exposure, presentation format, and practice exposure were matched to the intervention group.
| Study group | Role | What they receive |
|---|---|---|
| DISCORD-Dx Training | Experimental | Participants received a 20-minute standardized orientation on AI capabilities and limitations, confident error, verification, privacy, bias, and professional oversight, followed by a 40-minute structured DISCORD-Dx training module. The module covered seven steps: Diagnose independently; Inspect AI advice; Substantiate evidence; Classify disagreement; Override, modify, or accept; Review outcome; and Demonstrate transfer. Receives: Behavioral: DISCORD-Dx Training |
| Conventional AI-Literacy Training | Active comparator | Participants received the same 20-minute standardized orientation on AI capabilities and limitations, confident error, verification, privacy, bias, and professional oversight, followed by 40 minutes of conventional AI-literacy reinforcement and matched case discussion without the DISCORD-Dx mnemonic, disagreement taxonomy, or explicit accept/modify/override sequence. Receives: Behavioral: Conventional AI-Literacy Training |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of Case-Level Decisions Demonstrating Appropriate Reliance on AI Recommendations
Appropriate reliance was assessed at the case level and defined as following a correct AI recommendation or resisting an erroneous AI recommendation. Each participant completed eight unseen specialty-specific cases during the immediate assessment, including four cases with correct AI recommendations and four with erroneous AI recommendations. The outcome was expressed as the proportion of case-level decisions meeting the definition of appropriate reliance.
Measured: Immediately after training, during the 8-case post-training assessment
Secondary outcomes (7)
Proportion of Eligible Erroneous-AI Case Decisions With Harmful Switching
Harmful switching was assessed among erroneous-AI cases in which the participant's pre-AI diagnosis was correct. A harmful switch occurred when the final diagnosis became incorrect and was concordant with the erroneous AI recommendation.
Measured: Immediately after training, during the 8-case post-training assessment
Proportion of Erroneous-AI Case Decisions Demonstrating Over-Reliance
Over-reliance was defined, among cases containing an erroneous AI recommendation, as a final participant response that was concordant with the erroneous AI recommendation. The outcome was expressed as the proportion of eligible erroneous-AI case decisions showing over-reliance.
Measured: Immediately after training, during the 8-case post-training assessment
Proportion of Correct-AI Case Decisions Demonstrating Under-Reliance
Under-reliance was defined, among cases containing a correct AI recommendation, as a final participant response that was not concordant with the correct AI recommendation. The outcome was expressed as the proportion of eligible correct-AI case decisions showing under-reliance.
Measured: Immediately after training, during the 8-case post-training assessment
Proportion of Eligible Correct-AI Case Decisions With Beneficial Correction
Beneficial correction was assessed among correct-AI cases in which the participant's pre-AI diagnosis was incorrect. A beneficial correction occurred when the final diagnosis became correct and was concordant with the correct AI recommendation.
Measured: Immediately after training, during the 8-case post-training assessment
Final Diagnostic Accuracy
Final diagnostic accuracy was defined as agreement of the participant's final post-AI diagnosis or interpretation with the locked expert reference standard. The outcome was assessed across all immediate assessment cases.
Measured: Immediately after training, during the 8-case post-training assessment
Decision Time
Decision time was the total time from initial display of the case to submission of the final post-AI response. It was recorded electronically for each valid immediate assessment case.
Measured: Immediately after training, during the 8-case post-training assessment
Appropriate Reliance on AI Recommendations at 8 Weeks
Appropriate reliance at follow-up was defined as following a correct AI recommendation or resisting an erroneous AI recommendation. Participants completed six new specialty-specific cases, including three with correct and three with erroneous AI recommendations, without refresher training.
Measured: 8 weeks after training (target window ±7 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Sep 2026
- Study start6 Jun 2026
- Primary completion8 Jul 2026
- Study completion2 Sep 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial Intelligence (AI) in DiagnosisStudies 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
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Participants were randomized 1:1 to one of two parallel groups: DISCORD-Dx training or a time-matched conventional AI-literacy control. Randomization was stratified by specialty. Participants remained in their assigned group throughout the study, with no crossover. Immediate post-training assessment and 8-week follow-up used the same randomized group assignment.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 46 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sara Reza
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Sara Reza, Quaid-e-Azam Medical College
- Investigators
- Sara Reza, Principal investigator, Quaid-e-Azam Medical College
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07844889
- Sponsor's protocol ID
- QAMC-DISCORD-Dx-2026
- Acronym
- DISCORD-Dx
- Status last verified
- Sep 2026
- First submitted
- 15 Sep 2026
- Last updated
- 28 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Deidentified individual participant data underlying the published results will be shared, including participant-level data and eligible case-level data used in the primary, secondary, and exploratory analyses. Data will be shared without direct personal identifiers and will be accompanied by a data dictionary and relevant analysis documentation.
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- DISCORD-Dx as a Cognitive Strategy for Calibrated Reliance on AI in Diagnosis
- ClinicalTrials.gov ID
- NCT07844889
- Acronym / sponsor ID
- DISCORD-Dx / QAMC-DISCORD-Dx-2026
- Keywords
- Artificial Intelligence, Calibrated Reliance, Automation Bias, Human-AI Interaction, Diagnostic Decision-Making
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 46 participants (actual)
- Lead sponsor
- Sara RezaUniversity, hospital or other
- Study start
- 6 Jun 2026 (actual)
- Primary completion
- 8 Jul 2026 (actual)
- Study completion
- 2 Sep 2026 (actual)
- First posted
- 28 Sep 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.
This randomized study evaluated whether a structured reasoning strategy called DISCORD-Dx could help specialist doctors use artificial intelligence (AI) recommendations more safely during diagnostic decision-making. The strategy was designed to help specialists benefit from correct AI advice while resisting plausible but incorrect AI recommendations.
Forty-six consultant specialists in pathology and diagnostic radiology from two hospitals in Bahawalpur, Pakistan, were randomly assigned to receive either DISCORD-Dx training or time-matched conventional AI-literacy training. During assessment, participants first recorded and locked their own diagnosis before seeing a standardized AI recommendation. They then reviewed the AI advice and entered a final diagnosis. The AI recommendations included both correct recommendations and deliberately generated plausible errors that had been independently reviewed by specialists.
The main outcome was appropriate reliance on AI, defined as following correct AI advice or resisting erroneous AI advice immediately after training. Other outcomes included harmful switching from a correct diagnosis to an incorrect diagnosis after erroneous AI advice, over-reliance, under-reliance, final diagnostic accuracy, decision time, and appropriate reliance at 8 weeks. No participant interacted with a live AI system, and study responses did not affect patient care.
Detailed description
DISCORD-Dx is a seven-step cognitive strategy designed to support calibrated reliance on AI during diagnostic decision-making: Diagnose independently; Inspect the AI recommendation; Substantiate the evidence; Classify the disagreement; Override, modify, or accept; Review the outcome; and Demonstrate transfer.
Participants were consultant specialists with independent diagnostic responsibility in chemical pathology, hematology, microbiology, histopathology, or diagnostic radiology. Before randomization, participants completed four specialty-specific cases without AI. They were then randomized 1:1 to DISCORD-Dx training or an active control consisting of conventional AI-literacy training. Both groups received the same 20-minute orientation on AI limitations, confident error, verification, privacy, bias, and professional oversight, followed by a 40-minute group-specific training module.
Immediately after training, participants completed eight unseen specialty-specific cases, four containing correct AI recommendations and four containing deliberately erroneous but plausible AI recommendations. For each case, participants recorded an independent diagnosis before viewing the AI recommendation and then entered their final decision after reviewing the AI advice. At 8 weeks, participants completed six additional cases without refresher training. All AI recommendations were frozen and independently expert-reviewed before participant exposure, and participants did not interact with a live AI system.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- Participants were randomized 1:1 to one of two parallel groups: DISCORD-Dx training or a time-matched conventional AI-literacy control. Randomization was stratified by specialty. Participants remained in their assigned group throughout the study, with no crossover. Immediate post-training assessment and 8-week follow-up used the same randomized group assignment.
| Arm | Type | Receives |
|---|---|---|
| DISCORD-Dx Training | Experimental | Behavioral: DISCORD-Dx TrainingParticipants received a 20-minute standardized orientation on AI capabilities and limitations, confident error, verification, privacy, bias, and professional oversight, followed by a 40-minute structured DISCORD-Dx training module. The module covered seven steps: Diagnose independently; Inspect AI advice; Substantiate evidence; Classify disagreement; Override, modify, or accept; Review outcome; and Demonstrate transfer. |
| Conventional AI-Literacy Training | Active comparator | Behavioral: Conventional AI-Literacy TrainingParticipants received the same 20-minute standardized orientation on AI capabilities and limitations, confident error, verification, privacy, bias, and professional oversight, followed by 40 minutes of conventional AI-literacy reinforcement and matched case discussion without the DISCORD-Dx mnemonic, disagreement taxonomy, or explicit accept/modify/override sequence. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Practicing consultant specialist with current independent diagnostic responsibility.
- Specialty in chemical pathology, hematology, microbiology, histopathology, or diagnostic radiology.
Key exclusion criteria (5 listed)
- Trainee status.
- Absence of independent reporting responsibility.
- Direct involvement in constructing or validating the participant's specialty case bank.
- Prior access to assessment cases or scoring keys.
- Inability to complete the digital assessment.
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 |
|---|---|---|
| DISCORD-Dx TrainingA structured 40-minute cognitive training intervention delivered after a common 20-minute AI-safety orientation. The DISCORD-Dx module taught seven steps: Diagnose independently; Inspect AI advice; Substantiate evidence; Classify disagreement; Override, modify, or accept; Review outcome; and Demonstrate transfer. Guided practice cases were included, and delivery was standardized using locked scripts and fidelity checklists. | Behavioural | DISCORD-Dx Training |
| Conventional AI-Literacy TrainingA 40-minute active-control training intervention delivered after the same 20-minute AI-safety orientation. It reinforced conventional principles of AI literacy through matched case discussion but did not include the DISCORD-Dx mnemonic, disagreement taxonomy, or the explicit accept/modify/override sequence. Contact time, facilitator exposure, presentation format, and practice exposure were matched to the intervention group. | Behavioural | Conventional AI-Literacy Training |
Primary outcome
Proportion of Case-Level Decisions Demonstrating Appropriate Reliance on AI Recommendations
Time frame: Immediately after training, during the 8-case post-training assessment
Appropriate reliance was assessed at the case level and defined as following a correct AI recommendation or resisting an erroneous AI recommendation. Each participant completed eight unseen specialty-specific cases during the immediate assessment, including four cases with correct AI recommendations and four with erroneous AI recommendations. The outcome was expressed as the proportion of case-level decisions meeting the definition of appropriate reliance.
7 secondary outcomes
Proportion of Eligible Erroneous-AI Case Decisions With Harmful Switching
Time frame: Immediately after training, during the 8-case post-training assessment
Proportion of Erroneous-AI Case Decisions Demonstrating Over-Reliance
Time frame: Immediately after training, during the 8-case post-training assessment
Proportion of Correct-AI Case Decisions Demonstrating Under-Reliance
Time frame: Immediately after training, during the 8-case post-training assessment
Proportion of Eligible Correct-AI Case Decisions With Beneficial Correction
Time frame: Immediately after training, during the 8-case post-training assessment
Final Diagnostic Accuracy
Time frame: Immediately after training, during the 8-case post-training assessment
Decision Time
Time frame: Immediately after training, during the 8-case post-training assessment
Appropriate Reliance on AI Recommendations at 8 Weeks
Time frame: 8 weeks after training (target window ±7 days)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Sep 2026.
- Individual participant dataSharing planned: Yes. Deidentified individual participant data underlying the published results will be shared, including participant-level data and eligible case-level data used in the primary, secondary, and exploratory analyses. Data will be shared without direct personal identifiers and will be accompanied by a data dictionary and relevant analysis documentation.
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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. This study is not recruiting at the moment.
Sara Reza principal investigator
Quaid-e-Azam Medical College
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing DISCORD-Dx Training and Conventional AI-Literacy Training in people with Artificial Intelligence (AI) in Diagnosis and Decision Making, run by Sara Reza at 1 site in Pakistan.
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 5 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: finished, as of the sponsor's last update on 28 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Pakistan. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion of Case-Level Decisions Demonstrating Appropriate Reliance on AI Recommendations (time frame: Immediately after training, during the 8-case post-training assessment). 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 28 Sep 2026.