Artificial Intelligence for Rare Disease Diagnosis
3 de junio de 2026 actualizado por: Shuyang Zhang, MD, PhD, Peking Union Medical College Hospital
A Multicentre, Randomised Diagnostic Accuracy Study Evaluating AI Assisted Diagnosis of Rare Diseases
A multicentre, randomised diagnostic accuracy study to evaluate whether the rare disease-specific AI can improve diagnostic accuracy and efficiency for physicians managing real-world clinical cases.
Descripción general del estudio
Estado
Aún no reclutando
Condiciones
Intervención / Tratamiento
Descripción detallada
Rare diseases collectively affect approximately 300 million individuals worldwide.
This prolonged diagnostic delay is attributable in large part to the breadth of over 7,000 recognized rare conditions, which far exceeds the clinical exposure of any individual physician.
A rare disease-specific diagnostic AI was developed by Peking Union Medical College Hospital (PUMCH), supporting differential diagnosis generation, clinical workup planning, and genomic variant interpretation.
A balanced crossover design ensures that each enrolled physician serves as their own control, substantially reducing confounding from inter-reader variability in baseline diagnostic competency.
Within each physician, cases are randomly assigned at the case level to either the AI-assisted or unassisted condition, such that each physician reads a subset of cases with AI assistance and the remaining cases without.
This within-reader, case-level randomization eliminates the need for a washout period and directly controls for inter-reader differences in baseline diagnostic competency.
All cases are collected from real-world clinical settings with independently confirmed gold-standard diagnoses and span a pre-specified spectrum of rare and non-rare disease categories, reflecting the differential diagnostic challenge encountered in routine clinical practice, to ensure diagnostic breadth and clinical representativeness.
Physician seniority (junior vs. senior) is incorporated as a pre-specified stratification and subgroup analysis variable.
Diagnostic outputs are evaluated by an independent Expert Adjudication Committee, blinded to the assistance condition, using standardized scoring criteria established prior to data collection.
Tipo de estudio
Intervencionista
Inscripción (Estimado)
150
Fase
- No aplica
Contactos y Ubicaciones
Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.
Estudio Contacto
- Nombre: Shuyang Zhang
- Número de teléfono: +86-13911667211
- Correo electrónico: shuyangzhang103@163.com
Ubicaciones de estudio
-
-
-
Beijing, Porcelana
- Peking Union Medical College Hospital
-
Contacto:
- Shuyang Zhang
- Número de teléfono: +8613911667211
- Correo electrónico: shuyangzhang103@163.com
-
Cangzhou, Porcelana
- Cangzhou Central Hospital
-
Contacto:
- Yong Li
- Número de teléfono: +86-0317-2075013
- Correo electrónico: czszxyyirb@163.com
-
Changchun, Porcelana
- Changchun Sacred Heart Hospital
-
Contacto:
- Junbiao Cui
- Número de teléfono: +86-0431-88711699
- Correo electrónico: ccsx_net2008@126.com
-
Dongguan, Porcelana
- Dongguan People's Hospital
-
Contacto:
- Yaoqing Yuan
- Número de teléfono: +86-0769-28637333
- Correo electrónico: dgrmyyirb@163.com
-
Foshan, Porcelana
- First People's Hospital of Foshan
-
Contacto:
- Jun Jiang
- Número de teléfono: +86-0757-83833633
- Correo electrónico: jinye0320@163.com
-
Guiyang, Porcelana
- Guizhou Provincial People's Hospital
-
Contacto:
- Yan Zha
- Número de teléfono: +86-0851-85937094
- Correo electrónico: sytsb606@sina.com
-
Jilin City, Porcelana
- Jilin Central General Hospital
-
Contacto:
- Mingyu Shao
- Número de teléfono: +86-0432-62456181
- Correo electrónico: jlszxyy@163.com
-
Kunming, Porcelana
- The First People's Hospital of Yunnan Province
-
Contacto:
- Jianhong Hou
- Número de teléfono: +86-0871-63638800
- Correo electrónico: khyyyb@163.com
-
Lhasa, Porcelana
- Tibet Autonomous Region People's Hospital
-
Contacto:
- Dong Wu
- Número de teléfono: +86-0891-6371322
- Correo electrónico: wudong@pumch.cn
-
Tianjin, Porcelana
- Tianjin Children's Hospital
-
Contacto:
- Wei Liu
- Número de teléfono: 022-87787101
- Correo electrónico: setyyyb@tj.gov.cn
-
Wuhai, Porcelana
- Wuhai People's Hospital
-
Contacto:
- Rui Ren
- Número de teléfono: +86-0473-2035041
- Correo electrónico: whsrmyybgs@163.com
-
Xining, Porcelana
- Qinghai Provincial People's Hospital
-
Contacto:
- Qiang Zhang
- Número de teléfono: +86-0971-8177911
- Correo electrónico: 403252559@qq.com
-
Zhangzhou, Porcelana
- Zhangzhou Municipal Hospital of Fujian Province
-
Contacto:
- Xiao Yang
- Número de teléfono: +86-0596-2082950
- Correo electrónico: zzfh2005@126.com
-
-
Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Sí
Descripción
Inclusion Criteria:
- 1. Licensed physicians at the junior or senior level affiliated with internal medicine, neurology, pediatrics, and rare disease-related departments.
- 2. Willingness to provide written informed consent, adhere to trial protocols, and complete all required pre-study training prior to enrollment.
Exclusion Criteria:
- 1. Prior exposure to any of the clinical cases included in the study case library.
- 2. Direct participation in the design or development of the AI model.
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Diagnóstico
- Asignación: Aleatorizado
- Modelo Intervencionista: Asignación cruzada
- Enmascaramiento: Único
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
|---|---|
|
Experimental: Intervention Arm
Physicians complete assigned diagnostic tasks with the assistance of AI system in addition to conventional clinical resources.
|
A rare disease-specific diagnostic AI model is used to accept free text input and assist in rare disease diagnoses.
During the experimental condition, physicians may interact with the system freely alongside standard clinical resources to support their diagnostic reasoning.
|
|
Sin intervención: Control Arm
Physicians complete the assigned diagnostic tasks using conventional clinical resources only (e.g., medical databases and literature), without access to any generative AI tools.
This arm reflects routine clinical diagnostic practice.
|
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Top-3 Diagnostic Accuracy
Periodo de tiempo: Up to 60 minutes per case (from case presentation to diagnostic report submission).
|
The percentage of definitive diagnosis is included within the physician's top 3 choices.
|
Up to 60 minutes per case (from case presentation to diagnostic report submission).
|
Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Diagnosis Time per Case
Periodo de tiempo: Up to 60 minutes per case (from case presentation to diagnostic report submission).
|
Elapsed time from initial case presentation to final diagnostic report submission, recorded automatically via system logs.
|
Up to 60 minutes per case (from case presentation to diagnostic report submission).
|
|
Workup Plan Quality
Periodo de tiempo: Up to 60 minutes per case (from case presentation to diagnostic report submission).
|
Quality score of the clinical workup plan assigned by an independent expert committee using a standardized Likert Scale.
Scores range from 1 to 10, with higher scores indicating better workup plan quality.
|
Up to 60 minutes per case (from case presentation to diagnostic report submission).
|
|
Physician Reported Usability of the AI-Assisted Diagnostic System
Periodo de tiempo: Up to 60 minutes per case (upon completion of each case reading).
|
Physician-reported usability of the AI system, assessed after completion of each AI-assisted case reading using a 10-point physician-rated usability scale.
Scores range from 1 to 10, with higher scores indicating better system usability.
|
Up to 60 minutes per case (upon completion of each case reading).
|
|
Physician Reported Workload
Periodo de tiempo: Up to 60 minutes per case (upon completion of each case reading).
|
Task-related workload experienced by physicians, assessed after completion of each AI-assisted case reading using a 10-point Physician Workload Likert scale.
Scores range from 1 to 10, with higher scores indicating a higher workload.
|
Up to 60 minutes per case (upon completion of each case reading).
|
|
Physician Satisfaction
Periodo de tiempo: Up to 60 minutes per case (upon completion of each case reading).
|
Overall satisfaction of physicians with the diagnostic workflow, assessed after completion of each AI-assisted case reading using a 10-point Satisfaction Likert scale.
Scores range from 1 to 10, with higher scores indicating higher satisfaction.
|
Up to 60 minutes per case (upon completion of each case reading).
|
|
Physician Intention to Adopt AI-Assisted Diagnostic Support
Periodo de tiempo: Up to 60 minutes per case (upon completion of each case reading).
|
Physician willingness to integrate AI system into routine clinical practice, assessed after completion of each AI-assisted case reading using a 10-point Adoption Intention Likert scale.
Scores range from 1 to 10, with higher scores indicating higher adoption intention.
|
Up to 60 minutes per case (upon completion of each case reading).
|
Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Colaboradores
Investigadores
- Investigador principal: Shuyang Zhang, Peking Union Medical College Hospital
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Estimado)
20 de junio de 2026
Finalización primaria (Estimado)
1 de diciembre de 2026
Finalización del estudio (Estimado)
1 de junio de 2027
Fechas de registro del estudio
Enviado por primera vez
28 de mayo de 2026
Primero enviado que cumplió con los criterios de control de calidad
3 de junio de 2026
Publicado por primera vez (Actual)
4 de junio de 2026
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
4 de junio de 2026
Última actualización enviada que cumplió con los criterios de control de calidad
3 de junio de 2026
Última verificación
1 de junio de 2026
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- PUMCH I-23PJ948
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .