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| Name | Class |
|---|---|
| Affiliated Hospital of North Sichuan Medical College | OTHER |
| University of Glasgow | OTHER |
| Peking University | OTHER |
| Peking University First Hospital |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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. |
| |
| 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. |
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| Best Large Language Model Multidisciplinary Treatment Group |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| GPT-4o | Diagnostic 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Consultation Cost ($) | 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) | 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) | 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) | 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) | 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) | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Ethical Compliance (Boolean) | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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From hospital
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zining Luo, Doctor | Contact | 86 + 18161007029 | cblzn@nsmc.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Affiliated Hospital of North Sichuan Medical College | Nanchong | Sichuan | 637000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 21182434 | Result | 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. |
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| ID | Term |
|---|---|
| D009369 | Neoplasms |
| D012131 | Respiratory Insufficiency |
| D006331 | Heart Diseases |
| D007239 | Infections |
| D011014 | Pneumonia |
| D004194 | Disease |
| ID | Term |
|---|---|
| D012120 | Respiration Disorders |
| D012140 | Respiratory Tract Diseases |
| D002318 | Cardiovascular Diseases |
| D012141 | Respiratory Tract Infections |
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| OTHER |
| Beijing Institute of Petrochemical Technology | UNKNOWN |
| Case Western Reserve University | OTHER |
| Monash University | OTHER |
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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. |
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| GPT-4o mini | Diagnostic 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. |
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| MedicalGPT | Diagnostic 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. |
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| Claude-3.5 Sonnet | Diagnostic 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. |
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| Claude 3 Haiku | Diagnostic 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. |
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| Real Doctors | Diagnostic 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. |
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| 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 | 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. |
| D008171 | Lung Diseases |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |