Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06627985
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-10-01
Brief Title:
Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models A Parallel Controlled Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MDTALLM
Brief 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
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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None