Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models

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.

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.

Conditions

• Cancer
• Respiratory Failure
• Heart Diseases
• Infections
• Pneumonia
• Disease

Interventions

DIAGNOSTIC_TEST

GPT-4o

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.

DIAGNOSTIC_TEST

GPT-4o mini

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.

DIAGNOSTIC_TEST

MedicalGPT

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.

DIAGNOSTIC_TEST

Claude-3.5 Sonnet

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.

DIAGNOSTIC_TEST

Claude 3 Haiku

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.

DIAGNOSTIC_TEST

Real Doctors

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.

Sponsors & Collaborators

• Affiliated Hospital of North Sichuan Medical College

  collaborator OTHER

• University of Glasgow

  collaborator OTHER

• Peking University

  collaborator OTHER

• Peking University First Hospital

  collaborator OTHER

• Beijing Institute of Petrochemical Technology

  collaborator UNKNOWN

• Case Western Reserve University

  collaborator OTHER

• Monash University

  collaborator OTHER

• North Sichuan Medical College

  lead OTHER

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-10-01

Primary Completion

2024-11-01

Completion

2024-11-01

Countries

• China

Study Locations