The Affiliated Hospital of North Sichuan Medical College
Nanchong, Sichuan, 637000, China
Not Yet Recruiting
NCT Number: NCT06627985
Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models
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.
Not Yet Recruiting
Trial opening soon.
Get NotifiedKey information
Conditions
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- 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.
Exclusion criteria
- 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.
Treatment and study plan
GPT-4o
Diagnostic TestInput 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 mini
Diagnostic TestInput 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.
MedicalGPT
Diagnostic TestInput 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 Sonnet
Diagnostic TestInput 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 Haiku
Diagnostic TestInput 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 Doctors
Diagnostic TestRetrospectively collect the diagnostic and treatment recommendations from the corresponding departments involved in the multidisciplinary treatment of past patients, as well as the overall recommendations.
Primary outcomes
-
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.
Secondary outcomes
-
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.
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
North Sichuan Medical College
Other
Collaborators
- Affiliated Hospital of North Sichuan Medical College
- Beijing Institute of Petrochemical Technology
- Case Western Reserve University
- Monash University
- Peking University
- Peking University First Hospital
- University of Glasgow
Registry information
Official study title
Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models: A Parallel Controlled Study
Acronym: MDTALLM
Important dates
- Study start
- 2024
- Primary completion
- 2024
- Study completion
- 2024
- First posted
- Oct 4, 2024
- Registry last updated
- Oct 4, 2024
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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