CancerMDTALLMNCT06627985
Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models
An observational study of Cancer and Respiratory Failure, run by North Sichuan Medical College at 1 site in China.
Trial summary
This retrospective clinical trial aims to better explore the potential of large language models in medicine by comparing the effectiveness of MDT consultations conducted by human doctors with those conducted by large language models. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cancer, Respiratory Failure, Heart Diseases
- What is being tested?
- GPT-4o, GPT-4o mini and MedicalGPT
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2024to 1 Nov 2024
- Phase
- Observational
- 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
- No healthy volunteers
- Child, Adult, Older adult
Study population: From hospital
Who may be able to join
- 1. The medical records include interdisciplinary consultation notes, with recommendations from specialists of various departments and a well-documented final summary.
- 2. The medical records contain data from at least one year prior to and one year following the consultation (including intact reports and imaging records).
- 3. The patient\'s discharge conditions improved due to the multidisciplinary treatment plan after the consultation.
Who cannot join
- 1. The medical records do not include multidisciplinary consultation notes, or the recommendations from various departmental physicians and the final summary notes are incomplete or inadequate.
- 2. The medical records lack data from 1 year before and after the consultation, or miss necessary reports and imaging data, resulting in incomplete documentation.
- 3. The patient\'s condition at discharge has not improved following the multidisciplinary treatment plan, or the condition has worsened.
Trial locations and contacts
1 site in 1 country.
China1 site
The Affiliated Hospital of North Sichuan Medical College
Nanchong, Sichuan, 637000
Zining Luo, 86 + 18161007029, cblzn@nsmc.edu.cn
About this study
Official title: Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models: A Parallel Controlled Study
This retrospective clinical trial aims to better explore the potential of large language models in medicine by comparing the effectiveness of MDT consultations conducted by human doctors with those conducted by large language models.
The main questions to be addressed are:
Read more
Does using large language models to conduct anthropomorphic MDT consultations yield better results than using non-anthropomorphic processes? Is there a significant performance gap between MDT consultations conducted by large language models and those conducted by humans? How much greater is the economic benefit of MDT consultations from large language models compared to those conducted by humans?
Retrospectively collect MDT consultation records from the past 20 years in northern Sichuan in China, as well as anonymized patient medical records. Group 1: Different large language models are assigned to act as doctors from different departments and as MDT secretaries to summarize consultations. Group 2: The large language model directly outputs diagnostic and treatment recommendations for patients. Compare the outputs of groups 1 and 2 with human performance retrospectively, score them, and select the best model from each department for a re-evaluation through anthropomorphic MDT consultations, once again comparing them to human results.
- Conditions
- Cancer, Respiratory Failure, Heart Diseases, Infections, Pneumonia, Disease
- Being tested
- GPT-4o, GPT-4o mini, MedicalGPT, Claude-3.5 Sonnet, Claude 3 Haiku
- Medical subject headings
- Neoplasms, Respiratory Insufficiency, Heart Diseases, Infections, Pneumonia, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
GPT-4oDiagnostic test
Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
GPT-4o miniDiagnostic test
Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o mini. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
MedicalGPTDiagnostic test
Input all patient medical records, including text, examination reports, and imaging data, into MedicalGPT. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
Claude-3.5 SonnetDiagnostic test
Input all patient medical records, including text, examination reports, and imaging data, into Claude-3.5 Sonnet. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
Claude 3 HaikuDiagnostic test
Input all patient medical records, including text, examination reports, and imaging data, into Claude 3 Haiku. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
Real DoctorsDiagnostic test
Retrospectively collect the diagnostic and treatment recommendations from the corresponding departments involved in the multidisciplinary treatment of past patients, as well as the overall recommendations.
| Study group | Role | What they receive |
|---|---|---|
| Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group | Using a locally deployed MedicalGPT, the commercially available online GPT-4o, Claude-3.5 Sonnet, GPT-4o mini, and Claude 3 Haiku, will each sequentially play the role of physicians from different departments involved in the Multi-Disciplinary Treatment Process. They will then sequentially take on the role of a summarizer to compile their recommendations into a final suggestion or treatment plan. Receives: Diagnostic Test: GPT-4o; Diagnostic Test: GPT-4o mini; Diagnostic Test: MedicalGPT; Diagnostic Test: Claude-3.5 Sonnet; Diagnostic Test: Claude 3 Haiku | |
| Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group | Using a locally deployed MedicalGPT, the commercial online GPT-4o, Claude-3.5 Sonnet, GPT-4o mini, and Claude 3 Haiku to output multidisciplinary consultation results in a single instance, without separately assuming roles for each department and then compiling the results. Receives: Diagnostic Test: GPT-4o; Diagnostic Test: GPT-4o mini; Diagnostic Test: MedicalGPT; Diagnostic Test: Claude-3.5 Sonnet; Diagnostic Test: Claude 3 Haiku | |
| Real Doctors Multi-Disciplinary Treatment Group | In traditional multidisciplinary treatments, the results are documented in the consultation records of the patients involved, including the recommendations from doctors of various departments who participated in the consultation and the final summary by the secretary. Receives: Diagnostic Test: Real Doctors | |
| Best Large Language Model Multidisciplinary Treatment Group | After scoring the results of the Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group against the outcomes of the Real Doctors' Multi-Disciplinary Treatment Group on a department-by-department basis, the best substitute models and the best summary models for each department were selected. These top models are set to assume roles in a Multi-Disciplinary Treatment consultation. Receives: Diagnostic Test: GPT-4o; Diagnostic Test: GPT-4o mini; Diagnostic Test: MedicalGPT; Diagnostic Test: Claude-3.5 Sonnet; Diagnostic Test: Claude 3 Haiku |
What is being measured
The primary outcome is the main question the study is designed to answer.
Consultation Cost ($)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Consultation Time (min)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Comprehensiveness of the Multi-Disciplinary Treatment Results (Percentage Scale)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Clarity of Multi-Disciplinary Treatment Results (Percentage Scale)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Correctness of Multi-Disciplinary Treatment Results (Percentage Scale)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Cross-Professional Team Collaboration Practice Assessment (CPAT)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Rating Scale for Summarization
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Flesch-Kincaid Readability Test
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Secondary outcomes (1)
Ethical Compliance (Boolean)
Measured: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Oct 2024
- Study start1 Oct 2024 (estimated)
- Primary completion1 Nov 2024 (estimated)
- Study completion1 Nov 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for 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
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- North Sichuan Medical College
- Sponsor type
- University, hospital or other
- Collaborators
- Affiliated Hospital of North Sichuan Medical College, University of Glasgow, Peking University, Peking University First Hospital, Beijing Institute of Petrochemical Technology, Case Western Reserve University, Monash University
- Responsible party
- Principal investigator, Zining Luo, North Sichuan Medical College
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06627985
- Sponsor's protocol ID
- 1426887-2024-3
- Acronym
- MDTALLM
- Status last verified
- Oct 2024
- First submitted
- 1 Oct 2024
- Last updated
- 4 Oct 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
Schroder C, Medves J, Paterson M, Byrnes V, Chapman C, O'Riordan A, Pichora D, Kelly C. Development and pilot testing of the collaborative practice assessment tool. J Interprof Care. 2011 May;25(3):189-95. doi: 10.3109/13561820.2010.532620. Epub 2010 Dec 23.
PubMed 21182434
Study at a glance
From the registry record, last updated by the sponsor on 4 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models: A Parallel Controlled Study
- ClinicalTrials.gov ID
- NCT06627985
- Acronym / sponsor ID
- MDTALLM / 1426887-2024-3
- Conditions
- Cancer, Respiratory Failure, Heart Diseases, Infections, Pneumonia, Disease
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- North Sichuan Medical CollegeUniversity, hospital or other
- Collaborators
- Affiliated Hospital of North Sichuan Medical College, University of Glasgow, Peking University, Peking University First Hospital, Beijing Institute of Petrochemical Technology, Case Western Reserve University, Monash University
- Study start
- 1 Oct 2024 (estimated)
- Primary completion
- 1 Nov 2024 (estimated)
- Study completion
- 1 Nov 2024 (estimated)
- First posted
- 4 Oct 2024 (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 retrospective clinical trial aims to better explore the potential of large language models in medicine by comparing the effectiveness of MDT consultations conducted by human doctors with those conducted by large language models.
The main questions to be addressed are:
Does using large language models to conduct anthropomorphic MDT consultations yield better results than using non-anthropomorphic processes? Is there a significant performance gap between MDT consultations conducted by large language models and those conducted by humans? How much greater is the economic benefit of MDT consultations from large language models compared to those conducted by humans?
Retrospectively collect MDT consultation records from the past 20 years in northern Sichuan in China, as well as anonymized patient medical records. Group 1: Different large language models are assigned to act as doctors from different departments and as MDT secretaries to summarize consultations. Group 2: The large language model directly outputs diagnostic and treatment recommendations for patients. Compare the outputs of groups 1 and 2 with human performance retrospectively, score them, and select the best model from each department for a re-evaluation through anthropomorphic MDT consultations, once again comparing them to human results.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group | – | Diagnostic Test: GPT-4o, Diagnostic Test: GPT-4o mini, Diagnostic Test: MedicalGPT, Diagnostic Test: Claude-3.5 Sonnet, Diagnostic Test: Claude 3 HaikuUsing a locally deployed MedicalGPT, the commercially available online GPT-4o, Claude-3.5 Sonnet, GPT-4o mini, and Claude 3 Haiku, will each sequentially play the role of physicians from different departments involved in the Multi-Disciplinary Treatment Process. They will then sequentially take on the role of a summarizer to compile their recommendations into a final suggestion or treatment plan. |
| Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group | – | Diagnostic Test: GPT-4o, Diagnostic Test: GPT-4o mini, Diagnostic Test: MedicalGPT, Diagnostic Test: Claude-3.5 Sonnet, Diagnostic Test: Claude 3 HaikuUsing a locally deployed MedicalGPT, the commercial online GPT-4o, Claude-3.5 Sonnet, GPT-4o mini, and Claude 3 Haiku to output multidisciplinary consultation results in a single instance, without separately assuming roles for each department and then compiling the results. |
| Real Doctors Multi-Disciplinary Treatment Group | – | Diagnostic Test: Real DoctorsIn traditional multidisciplinary treatments, the results are documented in the consultation records of the patients involved, including the recommendations from doctors of various departments who participated in the consultation and the final summary by the secretary. |
| Best Large Language Model Multidisciplinary Treatment Group | – | Diagnostic Test: GPT-4o, Diagnostic Test: GPT-4o mini, Diagnostic Test: MedicalGPT, Diagnostic Test: Claude-3.5 Sonnet, Diagnostic Test: Claude 3 HaikuAfter scoring the results of the Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group against the outcomes of the Real Doctors' Multi-Disciplinary Treatment Group on a department-by-department basis, the best substitute models and the best summary models for each department were selected. These top models are set to assume roles in a Multi-Disciplinary Treatment consultation. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- From hospital
Key inclusion criteria (3 listed)
- 1. The medical records include interdisciplinary consultation notes, with recommendations from specialists of various departments and a well-documented final summary.
- 2. The medical records contain data from at least one year prior to and one year following the consultation (including intact reports and imaging records).
- 3. The patient\'s discharge conditions improved due to the multidisciplinary treatment plan after the consultation.
Key exclusion criteria (3 listed)
- 1. The medical records do not include multidisciplinary consultation notes, or the recommendations from various departmental physicians and the final summary notes are incomplete or inadequate.
- 2. The medical records lack data from 1 year before and after the consultation, or miss necessary reports and imaging data, resulting in incomplete documentation.
- 3. The patient\'s condition at discharge has not improved following the multidisciplinary treatment plan, or the condition has worsened.
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 |
|---|---|---|
| GPT-4oInput all patient medical records, including text, examination reports, and imaging data, into GPT-4o. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department. | Diagnostic test | Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group, Best Large Language Model Multidisciplinary Treatment Group, Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group |
| GPT-4o miniInput all patient medical records, including text, examination reports, and imaging data, into GPT-4o mini. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department. | Diagnostic test | Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group, Best Large Language Model Multidisciplinary Treatment Group, Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group |
| MedicalGPTInput all patient medical records, including text, examination reports, and imaging data, into MedicalGPT. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department. | Diagnostic test | Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group, Best Large Language Model Multidisciplinary Treatment Group, Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group |
| Claude-3.5 SonnetInput all patient medical records, including text, examination reports, and imaging data, into Claude-3.5 Sonnet. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department. | Diagnostic test | Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group, Best Large Language Model Multidisciplinary Treatment Group, Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group |
| Claude 3 HaikuInput all patient medical records, including text, examination reports, and imaging data, into Claude 3 Haiku. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department. | Diagnostic test | Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group, Best Large Language Model Multidisciplinary Treatment Group, Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group |
| Real DoctorsRetrospectively collect the diagnostic and treatment recommendations from the corresponding departments involved in the multidisciplinary treatment of past patients, as well as the overall recommendations. | Diagnostic test | Real Doctors Multi-Disciplinary Treatment Group |
Primary outcome
Consultation Cost ($)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Consultation Time (min)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Comprehensiveness of the Multi-Disciplinary Treatment Results (Percentage Scale)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Clarity of Multi-Disciplinary Treatment Results (Percentage Scale)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Correctness of Multi-Disciplinary Treatment Results (Percentage Scale)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Cross-Professional Team Collaboration Practice Assessment (CPAT)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Rating Scale for Summarization
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Flesch-Kincaid Readability Test
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
1 secondary outcome
Ethical Compliance (Boolean)
Time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Oct 2024.
- Results publication: PubMed 21182434Schroder C, Medves J, Paterson M, Byrnes V, Chapman C, O'Riordan A, Pichora D, Kelly C. Development and pilot testing of the collaborative practice assessment tool. J Interprof Care. 2011 May;25(3):189-95. doi: 10.3109/13561820.2010.532620. Epub 2010 Dec 23.
- Individual participant dataSharing planned: Yes
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cancer and Respiratory Failure, run by North Sichuan Medical College at 1 site 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 3 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: status not confirmed, as of the sponsor's last update on 4 Oct 2024. 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 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zining Luo, Doctor (86 + 18161007029, cblzn@nsmc.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Consultation Cost ($) (time frame: From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.). The study lists 8 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06627985, which also lists 1 linked publication.
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 4 Oct 2024.