NCT06627985

Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models

Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models: A Parallel Controlled Study

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 300 (estimated)
• Sponsor: North Sichuan Medical College · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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

Type	Name	Description
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.

Timeline

• Start date: 2024-10-01
• Primary completion: 2024-11-01
• Completion: 2024-11-01
• First posted: 2024-10-04
• Last updated: 2024-10-04

Locations

1 site across 1 country: China