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AI for Rare Disease Diagnosis in Real World

2026년 6월 14일 업데이트: Shuyang Zhang, MD, PhD, Peking Union Medical College Hospital

A Multicentre, Prospective, Cluster-Randomised, Parallel-Controlled Trial of a Rare-Disease Large Language Model in Real-World Clinical Settings

A multicentre, prospective, cluster-randomised, parallel-controlled real-world effectiveness study evaluating whether a rare-disease diagnostic large language model can improve diagnostic quality, efficiency, and health-economic outcomes for physicians managing patients with suspected rare or diagnostically unresolved disease.

연구 개요

상태

아직 모집하지 않음

정황

개입 / 치료

상세 설명

Rare disease patients commonly experience prolonged diagnostic odysseys rooted in limited rare disease recognition, phenotypic heterogeneity, and dispersed diagnostic clues. Diagnostic decision-support large language models may improve first-visit consultations by integrating prior records, generating structured analyses, and proposing candidate diagnoses, thereby shortening diagnostic pathways and improving appropriate genetic testing referral.

Here, rare diseases clinics are established at 12 clinical institutions using a two-level randomisation structure: (1) cluster randomisation of physicians stratified by seniority tier and centre, with physician arm fixed throughout to eliminate contamination; (2) eligible consented patients randomly assigned to consultation rooms, blinding patients to arm assignment and providing allocation concealment.

In the intervention arm, patients interact with the AI before their consultation; physicians will review the AI report and interact with AI when seeing patients. In the control arm, patients are seen under standard hospital workflow without any generative AI tools. Outcomes adjudicated by an independent Expert Committee blinded to arm assignment; adjudicators access no AI-generated materials.

연구 유형

중재적

등록 (추정된)

1055

단계

  • 해당 없음

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

  • 중국
      • Beijing, 중국
        • Peking Union Medical College Hospital
        • 연락하다:
      • Cangzhou, 중국
        • Cangzhou Central Hospital
        • 연락하다:
      • Changchun, 중국
        • Changchun Sacred Heart Hospital
      • Dongguan, 중국
        • Dongguan People's Hospital
      • Foshan, 중국
        • First People's Hospital of Foshan
      • Guiyang, 중국
        • Guizhou Provincial People's Hospital
      • Jilin City, 중국
        • Jilin Central General Hospital
      • Kunming, 중국
        • The First People's Hospital of Yunnan Province
      • Lhasa, 중국
        • Tibet Autonomous Region People's Hospital
      • Tianjin, 중국
        • Tianjin Children's Hospital
      • Wuhai, 중국
        • Wuhai People's Hospital
      • Xining, 중국
        • Qinghai Provincial People's Hospital
      • Zhangzhou, 중국
        • Zhangzhou Municipal Hospital of Fujian Province

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 어린이
  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

설명

Patient Inclusion Criteria:

  • Any age. Legal guardian co-signs consent for minors or individuals lacking legal capacity.
  • Diagnostically unresolved or suspected rare disease, with at least one prior complete clinical evaluation at a secondary-level or higher institution yielding no confirmed explanatory diagnosis.
  • First presentation to the enrolling institution for the current condition, with no prior records in the institutional HIS or outpatient system.
  • No prior genetic testing related to the current condition; no results or reports available.
  • Written informed consent provided voluntarily by patient or legal guardian, with commitment and ability to complete structured follow-up.

Patient Exclusion Criteria:

  • Confirmed diagnosis (clinical, pathological, or molecular) explaining the primary symptoms.
  • Emergency presentation, critical illness, or any condition incompatible with trial participation.
  • Neither patient nor legally authorised proxy able to complete follow-up.
  • Concurrent enrollment in another interventional study with diagnostic accuracy or genetic testing yield as a primary endpoint.
  • Prior use of another AI system has already yielded a confirmed diagnosis for the current condition.

Physician Inclusion Criteria

  • Licensed physician in internal medicine, neurology, pediatrics, general medicine, rare disease, or a related specialty.
  • ≥2 years of clinical practice; competent to manage rare disease patients; stratified into junior or senior tier.
  • Voluntary participation with written informed consent.

Physician Exclusion Criteria

  • No longer in clinical practice, or unable to fulfill required outpatient duties during the study period.
  • Unwilling to provide informed consent or to permit protocol-required collection of consultation and questionnaire data.
  • Currently enrolled in another AI-assisted clinical workflow, or expected to be unable to comply with the procedures.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 특수 증상
  • 할당: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: AI system
Patients and physicians will use the study AI system prior to and during the encounter in addition to conventional clinical workflow. Use of other generative AI tools is prohibited.
다른: AI system

Prior to consultation, the AI system will interact with patients to build a personalised medical profile, generate a structured clinical analysis, and recommend candidate diagnoses.

During the encounter, the AI system will interact with and be reviewed by physicians.
간섭 없음: Standard of care
Patients proceed directly to the consultation room without AI interaction. The attending physician conducts the encounter per standard hospital workflow using conventional clinical resources only. Use of any generative AI tool is prohibited.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Top-3 Candidate Diagnostic Accuracy
기간: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
The proportion of patients whose physician-provided up to 3 candidate diagnoses at the first study visit include at least one concordant with the final reference diagnosis (denominator: all randomised patients with a blinded-adjudicated confirmed reference diagnosis; intention-to-treat population).
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
Rare Genetic Disease Diagnostic Yield
기간: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
The proportion of all randomised patients who underwent genetic testing and had a clinically significant genetic variant (pathogenic or likely pathogenic) confirmed by an independent review committee blinded to arm assignment (denominator: all randomised patients). Variant pathogenicity classified per predefined ACMG/AMP guidelines.
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.

2차 결과 측정

결과 측정
측정값 설명
기간
Overall Diagnostic Yield
기간: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
Proportion of all randomised patients who receive a confirmed clinical diagnosis (confirmed by independent expert review) during the trial period.
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
Overall Diagnostic Accuracy
기간: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
Among patients with a confirmed clinical diagnosis, the proportion whose diagnosis is concordant with the blinded-adjudicated final reference diagnosis.
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
Time to Diagnosis
기간: From enrollment to the end of follow-up, up to 8 weeks.
Days from first visit to obtaining clinical diagnosis within the follow-up period. Patients without a confirmed diagnosis censored at end of follow-up.
From enrollment to the end of follow-up, up to 8 weeks.
Consultation Duration
기간: Assessed at each consultation (day 1), within 1 day.
In-room consultation time will be recorded, measured, and compared between arms. Pre-consultation AI interaction time and total visit time are additionally recorded for the intervention arm to fully characterise efficiency impact.
Assessed at each consultation (day 1), within 1 day.
Cost to Diagnosis
기간: From enrollment to the end of follow-up, up to 8 weeks.
Cumulative costs from first visit to confirmed diagnosis (or end of follow-up for undiagnosed patients), including direct medical costs, direct non-medical costs, and indirect costs.
From enrollment to the end of follow-up, up to 8 weeks.
Physician-Reported Experience
기간: Assessed at each consultation (day 1), within 1 day.
Physicians will assess their experience of the diagnostic workflow. Responses will be recorded using a standardized rating scale (range 1-5, where higher scores indicate more positive experience).
Assessed at each consultation (day 1), within 1 day.
Patient-Reported Experience
기간: Assessed at each consultation (day 1), within 1 day.
Patients will assess their experience of the diagnostic workflow. Responses will be recorded using a standardized rating scale (range 1-5, where higher scores indicate more positive experience).
Assessed at each consultation (day 1), within 1 day.
Genetic Testing Referral Rate
기간: Assessed at each consultation (day 1), within 1 day.
Proportion of all randomised patients for whom the physician autonomously decides to refer for genetic testing, capturing the effect of AI intervention on referral behaviour.
Assessed at each consultation (day 1), within 1 day.
Positive Rate Among Referred Patients
기간: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
The proportion in whom a clinically significant (pathogenic or likely pathogenic) genetic variant was detected among patients who completed genetic testing.
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.
Cost-Effectiveness Analysis
기간: From the first visit to diagnosis or the end of follow-up, up to 8 weeks.
Incremental cost-effectiveness ratio (incremental cost per additional correct diagnosis) as primary metric combining diagnostic yield with cumulative medical costs.
From the first visit to diagnosis or the end of follow-up, up to 8 weeks.

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Peking Union Medical College Hospital

협력자

Guizhou Provincial People's Hospital
First People's Hospital of Foshan
Dongguan People's Hospital
Qinghai People's Hospital
Cangzhou Central Hospital
The First People's Hospital of Yunnan
Tibet Autonomous Region People's Hospital
Tianjin Children's Hospital
Zhangzhou Municipal Hospital

수사관

  • 수석 연구원: Shuyang Zhang, MD, PhD, Peking Union Medical College Hospital

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 6월 20일

기본 완료 (추정된)

2027년 7월 1일

연구 완료 (추정된)

2027년 12월 1일

연구 등록 날짜

최초 제출

2026년 6월 7일

QC 기준을 충족하는 최초 제출

2026년 6월 14일

처음 게시됨 (실제)

2026년 6월 16일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 6월 16일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 6월 14일

마지막으로 확인됨

2026년 6월 1일

추가 정보

이 연구와 관련된 용어

키워드

추가 관련 MeSH 약관

기타 연구 ID 번호

  • PUMCH I-26PJ0002

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

희귀병에 대한 임상 시험

AI system에 대한 임상 시험

유사한 임상시험 검색

스폰서 및 공동 작업자

건강 상태

약물 개입

CROs by country

CROs in New Zealand

정황

희귀 질병

약물 개입

식이 보충제

스폰서 / 협력자

위치

구독하다