Lung CancerNCT07504367
Large Language Models Assist in Tumor MDT
A study testing LLM assists in MDT report writing in people with Lung Cancer and Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 2 sites in China.
Trial summary
Multidisciplinary teams (MDTs) represent the gold standard for personalized tumor treatment, but they are limited by medical resources and accessibility Limitation. Although large language models (LLMs) have shown promise in medical reasoning, their multidisciplinary practicality in pan-cancer MDTs has not been fully… Read more about this study
- Who is it for?
- Ages 25 to 33 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer, Breast Cancer, Colorectal Cancer
- What is being tested?
- LLM assists in MDT report writing
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 60 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jan 2026to 31 Dec 2026
- 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 25 to 33 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- A junior doctor with a practicing physician qualification certificate.
- Oncologists, surgeons, radiation oncologists, radiologists and pathologists with 3 to 5 years of clinical experience.
- Age: 25 to 33 years old, gender not limited.
- During the research period, one can participate for no less than 10 hours.
- Agree to participate in this research and sign the informed consent form.
Who cannot join
- Have participated in the previous diagnosis and treatment of any one of the 20 cases included in the study.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, Guangdong, 510000
Herui Yao, PhD, +8613500018020, yaoherui@mail.sysu.edu.cn
Yufang Yu, +8613660238987, yuyf9@mail.sysu.edu.cn
- Recruiting
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, Guangdong
Yunfang Yu, Doctor, +8613660238987, yuyf9@mail.sysu.edu.cn
About this study
Official title: Evaluating Large Language Models as Decision Support Agents in Pan-Cancer Tumor Boards: A Randomized Controlled Trial
Multidisciplinary teams (MDTs) represent the gold standard for personalized tumor treatment, but they are limited by medical resources and accessibility Limitation. Although large language models (LLMs) have shown promise in medical reasoning, their multidisciplinary practicality in pan-cancer MDTs has not been fully explored. In the early stage of this project, LLMs with high clinical application efficacy were identified through benchmark tests, and an open-label randomized controlled study (RCT) was conducted based on these LLMs. The research aims to explore whether AI-assisted assistance can enhance the accuracy and writing efficiency of MDT diagnosis and treatment reports. This study intends to prospectively collect the diagnosis and treatment information of 20 patients and MDT diagnosis and treatment information. It is planned to recruit 40 junior doctors. Doctors in the intervention group will use LLM to assist in the writing of MDT reports, while doctors in the control group will use traditional information retrieval methods for the writing of MDT reports. Three clinical experts ultimately used a standardized Likert scale to conduct comprehensive and multidisciplinary scoring of the MDT reports of the intervention group and the control group. This study quantitatively compared the diagnosis and treatment quality and efficiency of the MDT AI-assisted model and the traditional model to verify the application potential of large language models in assisting tumor diagnosis and treatment.
- Conditions
- Lung Cancer, Breast Cancer, Colorectal Cancer, Stomach Cancer, Liver Cancer
- Being tested
- LLM assists in MDT report writing
- Medical subject headings
- Lung Neoplasms, Breast Neoplasms, Colorectal Neoplasms, Stomach Neoplasms, Liver Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
LLM assists in MDT report writingOther
This study was a prospective RCT, and the intervention content was an auxiliary tool for writing MDT reports. The intervention group used LLM to assist in the writing of MDT reports. The prescribed MDT medical records (excluding diagnosis and treatment opinions) were input into the LLM, and the output content could be used as a reference for the MDT report. Finally, the MDT diagnosis and treatment opinions were written under the personal judgment of the doctors. The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions.
| Study group | Role | What they receive |
|---|---|---|
| AI-MDT | Experimental | This study was a prospective RCT, and the intervention content was an auxiliary tool for writing MDT reports. The intervention group used LLM to assist in the writing of MDT reports. The prescribed MDT medical records (excluding diagnosis and treatment opinions) were input into the LLM, and the output content could be used as a reference for the MDT report. Finally, the MDT diagnosis and treatment opinions were written under the personal judgment of the doctors. Receives: Other: LLM assists in MDT report writing |
| Trad-MDT | No intervention | The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The overall score of the MDT report
Clinical experts comprehensively evaluated the diagnosis and treatment opinions of different departments in the MDT report, and used the standardized Likert scale to comprehensively score the MDT reports of the intervention group and the control group (1 to 5 points, the higher the better).
Measured: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
Secondary outcomes (5)
The radiation oncology score of the MDT report
Clinical experts used a standardized Likert scale to score the radiotherapy department's diagnosis and treatment opinions reported by the MDT in the intervention group and the control group (1 to 5 points, the higher the better).
Measured: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The medical oncology score of the MDT report
Clinical experts used the standardized Likert scale to score the medical oncology diagnosis and treatment opinions reported by the MDT in the intervention group and the control group (1 to 5 points, the higher the better).
Measured: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The pathology score of the MDT report
Clinical experts used the standardized Likert scale to score the pathological diagnosis and treatment opinions reported by the MDT in the intervention group and the control group (1 to 5 points, the higher the better).
Measured: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The radiology score of the MDT report
Clinical experts used a standardized Likert scale to score the radiology diagnosis and treatment opinions reported by the MDT in the intervention group and the control group (1 to 5 points, the higher the better).
Measured: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The time consumption in writing an MDT report
The intervention group and the control group completed the MDT report for each case at all times (unit: hours).
Measured: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2026
- Study start1 Jan 2026
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies 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
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Yunfang Yu, PhD, Study chair, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityHerui Yao, PhD, Study director, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07504367
- Sponsor's protocol ID
- SYSKY-2026-071-02
- Status last verified
- Mar 2026
- First submitted
- 11 Mar 2026
- Last updated
- 31 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Requests for the individual data or study documents will be considered where the proposed use aligns with public good purposes, does not conflict with other requests, and the requestor is willing to sign a data access agreement. Contact is though the corresponding author.
Study at a glance
From the registry record, last updated by the sponsor on 31 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating Large Language Models as Decision Support Agents in Pan-Cancer Tumor Boards: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07504367
- Acronym / sponsor ID
- SYSKY-2026-071-02
- Conditions
- Lung Cancer, Breast Cancer, Colorectal Cancer, Stomach Cancer, Liver Cancer
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 31 Mar 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.
Multidisciplinary teams (MDTs) represent the gold standard for personalized tumor treatment, but they are limited by medical resources and accessibility Limitation. Although large language models (LLMs) have shown promise in medical reasoning, their multidisciplinary practicality in pan-cancer MDTs has not been fully explored. In the early stage of this project, LLMs with high clinical application efficacy were identified through benchmark tests, and an open-label randomized controlled study (RCT) was conducted based on these LLMs. The research aims to explore whether AI-assisted assistance can enhance the accuracy and writing efficiency of MDT diagnosis and treatment reports. This study intends to prospectively collect the diagnosis and treatment information of 20 patients and MDT diagnosis and treatment information. It is planned to recruit 40 junior doctors. Doctors in the intervention group will use LLM to assist in the writing of MDT reports, while doctors in the control group will use traditional information retrieval methods for the writing of MDT reports. Three clinical experts ultimately used a standardized Likert scale to conduct comprehensive and multidisciplinary scoring of the MDT reports of the intervention group and the control group. This study quantitatively compared the diagnosis and treatment quality and efficiency of the MDT AI-assisted model and the traditional model to verify the application potential of large language models in assisting tumor diagnosis and treatment.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-MDT | Experimental | Other: LLM assists in MDT report writingThis study was a prospective RCT, and the intervention content was an auxiliary tool for writing MDT reports. The intervention group used LLM to assist in the writing of MDT reports. The prescribed MDT medical records (excluding diagnosis and treatment opinions) were input into the LLM, and the output content could be used as a reference for the MDT report. Finally, the MDT diagnosis and treatment opinions were written under the personal judgment of the doctors. |
| Trad-MDT | No intervention | –The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions. |
Who may qualify
- Ages
- 25 to 33 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (5 listed)
- A junior doctor with a practicing physician qualification certificate.
- Oncologists, surgeons, radiation oncologists, radiologists and pathologists with 3 to 5 years of clinical experience.
- Age: 25 to 33 years old, gender not limited.
- During the research period, one can participate for no less than 10 hours.
- Agree to participate in this research and sign the informed consent form.
Key exclusion criteria (1 listed)
- Have participated in the previous diagnosis and treatment of any one of the 20 cases included in the study.
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 |
|---|---|---|
| LLM assists in MDT report writingThis study was a prospective RCT, and the intervention content was an auxiliary tool for writing MDT reports. The intervention group used LLM to assist in the writing of MDT reports. The prescribed MDT medical records (excluding diagnosis and treatment opinions) were input into the LLM, and the output content could be used as a reference for the MDT report. Finally, the MDT diagnosis and treatment opinions were written under the personal judgment of the doctors. The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions. | Other | AI-MDT |
Primary outcome
The overall score of the MDT report
Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
Clinical experts comprehensively evaluated the diagnosis and treatment opinions of different departments in the MDT report, and used the standardized Likert scale to comprehensively score the MDT reports of the intervention group and the control group (1 to 5 points, the higher the better).
5 secondary outcomes
The radiation oncology score of the MDT report
Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The medical oncology score of the MDT report
Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The pathology score of the MDT report
Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The radiology score of the MDT report
Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
The time consumption in writing an MDT report
Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).
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 31 Mar 2026.
- Individual participant dataSharing planned: No. Requests for the individual data or study documents will be considered where the proposed use aligns with public good purposes, does not conflict with other requests, and the requestor is willing to sign a data access agreement. Contact is though the corresponding author.
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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.
Yunfang Yu, PhD study chair
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Herui Yao, PhD study director
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing LLM assists in MDT report writing in people with Lung Cancer and Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 2 sites in China.
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 5 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 31 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. 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?
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 Yunfang Yu, PhD (+8613660238987, yuyf9@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The overall score of the MDT report (time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).). 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 31 Mar 2026.