Clinical trial NCT06874647: Chest X-Ray Image Diagnosis and Report Generation Dedicated Model Based on Deepseek
Official title: Large Language Models for Chest X-Ray Image Diagnosis and Report Generation: A Stepwise, Multicenter, Early-Stage Clinical Validation Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Radiology
Artificial Intellegence
Large Language Model
Chest X-ray for Clinical Evaluation
Keywords: radiology
artificial intelligence
Large language model
Lead sponsor: Wuhan Union Hospital, China
Sponsor class: OTHER
First posted: 2025-03-13
Enrollment: 300
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients requiring chest X-ray

Brief summary: The global shortage of radiologists is a pressing issue, particularly in regions with limited medical resources. Against this backdrop, making report generation the core objective of radiology AI systems not only aligns with the practical needs of radiologists but also better serves patient requirements. With the development of multimodal large models, it has become possible to develop automatic report generation systems for medical images. Although ChatGPT 4o has demonstrated certain capabilities in multiple medical sub - fields, as a closed - source system, it has limitations. Its model generation mechanism is opaque, and issues such as hallucination exist. Recently, Deepseek's open - source multimodal large model, Janus - Pro, an "any to any" model, has the advantages of high performance, low cost, and open - source. Nature published three consecutive articles introducing its stunning features. After training and fine - tuning, Janus - Pro shows great potential in medical image diagnosis and report generation. However, currently, the application of Janus - Pro in image diagnosis has not been evaluated. Most existing models are highly versatile but lack optimization for specific domains, and there is a lack of systematic and multi - dimensional evaluation methods to determine the pros and cons of multimodal large models in medical radiology. Based on these current situations, the purpose of our research is to develop and verify the application value of large models dedicated to medical images in image diagnosis and radiology report generation.


Eligibility criteria: Inclusion Criteria:

1. Type of examination: standard digital orthopantomogram of the chest during the study period.
2. Completeness of information: Medical records containing basic information, medical history, etc.
3. Informed consent: written consent form signed by the patient or legal representative.

Exclusion Criteria:

1. Anatomical abnormalities: congenital abnormalities of chest development, history of chest surgery, or severe scoliosis that interfere with image interpretation.
2. Poor image quality: chest radiographs with severe artifacts, exposure abnormalities, or incomplete images.
3. Acutely ill: life-threatening and unable to cooperate with the study process.
4. Mental cognitive problems: suffering from severe mental illness or cognitive impairment, unable to understand the study and sign.
5. Pregnant: Fetus is radiosensitive and physiological changes during pregnancy affect the images.
6. Participation in other studies: concurrent participation in other programs that affect the results of this study.

Primary outcomes:
measure: Report Quality Score and agreement score
timeFrame: No more than 1 week
measure: Pairwise preference
timeFrame: No more than 1 week
measure: Reading Time
timeFrame: No more than 1 week
Secondary outcomes:
Other outcomes:
Locations:
city: Luoyang
country: China
facility: The First Affiliated Hospital of Henan University of science and technology
state: Henan
city: Zhengzhou
country: China
facility: The First Affiliated Hospital of Zhengzhou University
state: Henan
city: Wuhan
country: China
facility: Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
Study officials:
References:
citation: Tanno R, Barrett DGT, Sellergren A, Ghaisas S, Dathathri S, See A, Welbl J, Lau C, Tu T, Azizi S, Singhal K, Schaekermann M, May R, Lee R, Man S, Mahdavi S, Ahmed Z, Matias Y, Barral J, Eslami SMA, Belgrave D, Liu Y, Kalidindi SR, Shetty S, Natarajan V, Kohli P, Huang PS, Karthikesalingam A, Ktena I. Collaboration between clinicians and vision-language models in radiology report generation. Nat Med. 2025 Feb;31(2):599-608. doi: 10.1038/s41591-024-03302-1. Epub 2024 Nov 7.