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Not yet recruiting NCT07740044

Medical Large Language Model-Assisted Diagnosis and Treatment in Primary Care Chronic Disease Management

No phase Interventional Coronary Artery Disease Atrial Fibrillation (AF) Heart Failure Stroke

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Medical large language model.
Who it may be relevant to
Registry conditions: Coronary Artery Disease, Atrial Fibrillation (AF), Heart Failure, Stroke. Basic parameters: 18 years — 65 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Official title

Verification and Application Study of Medical Large Language Model-Assisted Diagnosis and Treatment in Primary Care Chronic Disease Management: A Randomized Controlled Study

Overview

This study aims to explore the feasibility of using medical large language models to assist in chronic disease management. By setting up experimental and control group interventions and having experts blindly evaluate anonymized cases, it compares different management approaches in chronic disease care, verifying the scientific basis, effectiveness, and potential for broader use of medical large language models in supporting chronic disease management.

Interventions

  • Device Medical large language model
    The primary care physicians in the intervention group completed the management decisions for chronic disease cases, including disease assessment, examination suggestions, treatment plans, selection of management methods, and health education, with the assistance of the medical LLM.

Primary outcome measures

  • expert overall scores [Time frame: through study completion, an average of 3 months.]
Secondary outcome measures (10)
  • accuracy of condition assessment [Time frame: through study completion, an average of 3 months.]
  • reasonableness of test recommendations [Time frame: through study completion, an average of 3 months.]
  • reasonableness of treatment plans [Time frame: through study completion, an average of 3 months.]
  • appropriateness of management choices [Time frame: through study completion, an average of 3 months.]
  • scientific nature of patient education content [Time frame: through study completion, an average of 3 months.]
  • feasibility at the primary care level [Time frame: through study completion, an average of 3 months.]
  • the overall clinical quality [Time frame: through study completion, an average of 3 months.]
  • risk advice [Time frame: through study completion, an average of 3 months.]
  • Any diagnostic or treatment information missing [Time frame: through study completion, an average of 3 months.]
  • feedback regarding the use of medical LLM by primary care physicians [Time frame: through study completion, an average of 3 months.]

Eligibility criteria

Inclusion criteria

  • Work experience of 3 years or more;
  • Able to complete the case assessment tasks required by the study;
  • Have a practicing doctor qualification;
  • Work at a township health center or community health service center;
  • Voluntarily participate in the study and sign the informed consent form.

Exclusion criteria

N/A

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Single blind
Primary purpose
Other

Study locations

China · 1 center
  • The Affiliated Taizhou People's Hospital of Nanjing Medical University — Taizhou

Identifiers

NCT: NCT07740044 · LSKY 2026-114-01

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗