FindTrial
Sign In

The value of large language models to assist in the diagnosis of unstructured text reports of chest CT

The value of large language models to assist in the diagnosis of unstructured text reports of chest CT - The value of large language models to assist in the diagnosis of unstructured text reports of chest CT

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400091330
Enrollment
Unknown
Registered
2024-10-25
Start date
2024-11-01
Completion date
Unknown
Last updated
2024-10-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Chest Diseases

Interventions

Multiple-choice questioning group:None
Open-ended questioning group:None

Sponsors

Zhujiang Hospital, Southern Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) The in-hospital diagnosis contained at least one of the 13 diagnoses of chronic obstructive pulmonary disease (COPD), pulmonary infection, liver cysts, coronary artery disease, gallbladder stones, pulmonary heart disease, tuberculosis, emphysema, fatty liver, pleural effusion, bronchiectasis, aortic coarctation, and pulmonary herpes; (2) The patient has undergone a chest CT examination and has a complete copy of the chest CT examination report. (3) 18-80 years old.

Exclusion criteria

Exclusion criteria: The text of the chest CT examination report was incomplete.

Design outcomes

Primary

MeasureTime frame
Subjective Answer Accuracy Rate;Reference Answer Accuracy Rate;—

Secondary

MeasureTime frame
Receiver Operating Characteristic Curve;Area Under the Curve;—

Countries

China

Contacts

Public ContactChen Xin

Zhujiang Hospital, Southern Medical University

chen_xin1020@163.com+86 139 0220 5193

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026