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Empowering Rare Disease Diagnosis: A Synthetic Data-Driven Reasoning LLM for Long-Tail Medical Challenge

Empowering Rare Disease Diagnosis: A Synthetic Data-Driven Reasoning LLM for Long-Tail Medical Challenge

Status
Active, not recruiting
Phases
Unknown
Study type
Interventional
Source
ChiCTR
Registry ID
ChiCTR2500115619
Enrollment
Unknown
Registered
2025-12-29
Start date
2026-01-01
Completion date
Unknown
Last updated
2026-02-16

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

Conditions

None

Interventions

Interventional group:With access to large language model and web search
Control group:With access to web search only

Sponsors

Tsinghua University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. physicians that pass the physician licensing exam; 2. physicians in in internal medicine, pediatrics, and neurology; 3. Physicians that are currently working in clinics 4. Physicians that finish the informed consent form.

Exclusion criteria

Exclusion criteria: 1. Physicians that from other departments; 2. Physicians that has quitted from clinics work; 3. Physicians that are unwilling to finish the informed consent form.

Design outcomes

Primary

MeasureTime frame
The accuracy of the differential diagnosis list (whether the final diagnosis list include the confirmed rare disease diagnosis);—

Secondary

MeasureTime frame
The subjective evalation of the helpfulness ;Time for the diagnosis;Diagnostic calibration;—

Countries

China

Contacts

Public ContactWong Tien Yin

Tsinghua Universiy

wongtienyin@tsinghua.edu.cn+86 151 0100 3732

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 19, 2026