Liver DiseasesNCT07445152
Research on Construction and Verification of Multimodal Medical Imaging Large Model
An observational study of Liver Diseases and Gallbladder Diseases, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
Trial summary
With the accumulation of multimodal clinical data such as medical imaging and electronic health records (EHRs), efficient utilization of multi-source information to achieve precise diagnosis and intelligent decision-making has become a core direction of medical artificial intelligence (AI). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Liver Diseases, Gallbladder Diseases, Pancreatic Diseases
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 15 Nov 2026to 15 Nov 2027
- How long will I take part?
- 4 Weeks
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: he study population was composed of adult patients who met the preset inclusion criteria and were free of any exclusion criteria. In terms of inclusion requirements, eligible patients were aged 18 years or older and had received imaging examinations including computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound at the hospital during the study period. The examination items were required to target the hepatobiliary and pancreatic system, which was the focus of the research. All enrolled patients needed to be equipped with at least complete imaging data and radiological diagnostic reports, with relevant medical record information serving as supplementary modalities. In addition, written informed consent must be obtained from the patients themselves or their legal representatives, who agreed that the de-identified data of the patients could be used for the validation of scientific research models. Finally, all case data of the selected patients had to pass strict qual
Who may be able to join
- Adult patients aged ≥ 18 years.
- Patients who underwent imaging examinations (CT, MRI, ultrasound, etc.) at this hospital during the study period.
- The examination items are consistent with the disease types or systems focused on by the study (hepatobiliary and pancreatic system).
- Possess at least complete imaging data, radiological diagnostic reports, with relevant medical record information as supplementary modalities.
- Patients and their legal representatives have signed an informed consent form, agreeing to the use of their de-identified data for scientific research model validation.
Who cannot join
- Patients who refuse to sign the informed consent form.
- Cases with unassessable images due to severe motion artifacts, incomplete scanning, or equipment abnormalities.
- Cases with severe deficiency of clinical data or failure to match with imaging data.
- Cases with special pathological conditions or post-operative status (e.g., extensive resection, significant structural changes after radiotherapy) that affect the consistency of model analysis.
- Data samples with privacy protection or legal risks.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
With the accumulation of multimodal clinical data such as medical imaging and electronic health records (EHRs), efficient utilization of multi-source information to achieve precise diagnosis and intelligent decision-making has become a core direction of medical artificial intelligence (AI). Although traditional unimodal algorithms have yielded outcomes in specific tasks, their inability to model the semantic correlations among imaging, textual, and laboratory data leads to insufficient stability and limited interpretability of diagnostic results, making it difficult to meet the needs of comprehensive decision-making in complex clinical scenarios.
In recent years, multimodal large models have demonstrated excellent cross-modal understanding and knowledge transfer capabilities in natural images and general vision-language tasks, providing a new paradigm for medical AI. However, direct application in medical scenarios still faces challenges: first, the medical semantic system differs significantly from general language models, hindering the accurate representation of disease characteristics and imaging details; second, the complex morphology of lesions and uneven sample distribution in medical data increase the difficulty of model generalization; third, clinical data involves privacy, so data security and ethical compliance serve as prerequisites for research.
Read more
The research on medical multimodal large models aims to integrate multi-source heterogeneous medical data, establish a unified semantic representation and reasoning mechanism, and realize full-process intelligent analysis including disease identification and lesion localization. This approach can not only improve the efficiency and accuracy of clinical diagnosis but also provide clinicians with interpretable and traceable auxiliary decision support, boasting broad application prospects.
Based on the hospital's clinical data resources and the research team's algorithmic foundation, this study intends to construct a multimodal large model system for medical imaging diagnosis, enabling closed-loop intelligent analysis from multimodal information fusion to diagnostic report generation. The research will strictly adhere to medical ethical standards, protect patients' right to information, right to privacy, and data security. Before the official launch of the project, ethical review must be passed, and relevant regulations shall be followed to ensure the unity of scientific research and ethics, laying a compliant foundation for subsequent clinical validation and promotion.
Read the detailed description
With the continuous accumulation of medical imaging, electronic health records (EHRs), and multimodal clinical data, how to efficiently leverage multi-source medical information to achieve precise diagnosis and intelligent decision-making has become a core direction in the development of medical artificial intelligence (AI). Although traditional unimodal algorithms (e.g., models based solely on CT, MRI, or ultrasound images) have yielded certain results in specific tasks, their inability to model semantic correlations among imaging, textual, and laboratory data often leads to insufficient stability and limited interpretability of diagnostic outcomes, making it difficult to meet the comprehensive decision-making needs of complex clinical scenarios.
In recent years, multimodal large language models (MLLMs) have demonstrated remarkable cross-modal understanding and knowledge transfer capabilities in natural image processing and general vision-language tasks, providing a new technical paradigm for medical AI. However, the direct application of such models in medical scenarios still faces multiple challenges: first, there are significant discrepancies between the medical semantic system and general language models, hindering the accurate representation of disease characteristics and imaging details; second, the complex morphology of lesions and imbalanced sample distribution in medical data increase the difficulty of model generalization; third, clinical data involves privacy-sensitive information, making data security and ethical compliance a prerequisite for research.
Research on medical multimodal large models aims to comprehensively utilize multi-source heterogeneous data-such as medical imaging (e.g., CT, MRI, X-ray), EHRs, and laboratory reports-to establish a unified semantic representation and reasoning mechanism, enabling end-to-end intelligent analysis including disease identification, lesion localization, report generation, and disease progression prediction. This research direction not only helps improve the efficiency and accuracy of clinical diagnosis but also provides clinicians with interpretable and traceable auxiliary decision support, boasting broad prospects for clinical application.
Based on the hospital's abundant clinical data resources and the research team's algorithm development foundation, this study intends to construct a multimodal large model system for medical imaging diagnosis, realizing a closed-loop intelligent analysis pipeline from multimodal information fusion to diagnostic report generation.
During the research implementation, strict adherence to medical ethical standards will be followed to fully protect patients' right to informed consent, privacy, and data security. To ensure the scientificity and compliance of the research design, this project must pass ethical review prior to its official launch. In accordance with relevant regulations including the Declaration of Helsinki, International Ethical Guidelines for Health-related Research Involving Humans, and Ethical Review Measures for Life Science and Medical Research Involving Humans, we will achieve the organic integration of scientific research and ethical principles, laying a compliant foundation for subsequent clinical validation and application promotion.
- Medical subject headings
- Liver Diseases, Gallbladder Diseases, Pancreatic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Group1:This study enrolled adult patients aged ≥ 18 years who underwent imaging examinations (includ | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Disease Diagnosis Task
The primary outcome is the accuracy and reliability of the multimodal imaging diagnostic model in identifying and classifying target diseases compared with the gold standard of clinical diagnosis by senior radiologists.
Measured: From enrollment to the end of diagnosis at 3 days
Lesion Localization and Segmentation Task
It includes the model's performance in precise localization, contour segmentation and quantitative measurement of lesions, assessed by Dice similarity coefficient, IoU and localization error.
Measured: from enrollment to end of diagnosis up to 3 days
Diagnostic Report Generation Task
It evaluates the clinical validity, completeness, consistency and readability of automatically generated radiology reports relative to manual reports.
Measured: from enrollment to end of diagnosis up to 3 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Mar 2026
- Study start15 Nov 2026 (estimated)
- Primary completion15 Nov 2027 (estimated)
- Study completion15 Nov 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver DiseasesStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 4 Weeks
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07445152
- Sponsor's protocol ID
- 研2025-1442
- Status last verified
- Nov 2025
- First submitted
- 9 Feb 2026
- Last updated
- 3 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The decision not to share individual participant data (IPD) from this study is primarily based on ethical, privacy protection, and legal compliance considerations, as well as the inherent characteristics of the research data involved. First and foremost, the core data of this study includes de-identified imaging materials, radiological diagnostic reports, and associated medical records of patients, which are closely linked to personal health information. Despite the implementation of de-identification procedures during data collection and collation, there remains a potential risk of re-identifying individual participants if the data are shared without strict restrictions. Such a risk would violate the stipulations of relevant privacy protection laws and regulations, as well as the informed consent signed by the patients and their legal representatives. It should be emphasized that the informed consent clearly specifies that the de-identified data of participants is only used for the in
Study at a glance
From the registry record, last updated by the sponsor on 3 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Construction and Verification of Multimodal Medical Imaging Large Model
- ClinicalTrials.gov ID
- NCT07445152
- Acronym / sponsor ID
- 研2025-1442
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 15 Nov 2026 (estimated)
- Primary completion
- 15 Nov 2027 (estimated)
- Study completion
- 15 Nov 2027 (estimated)
- First posted
- 3 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
With the accumulation of multimodal clinical data such as medical imaging and electronic health records (EHRs), efficient utilization of multi-source information to achieve precise diagnosis and intelligent decision-making has become a core direction of medical artificial intelligence (AI). Although traditional unimodal algorithms have yielded outcomes in specific tasks, their inability to model the semantic correlations among imaging, textual, and laboratory data leads to insufficient stability and limited interpretability of diagnostic results, making it difficult to meet the needs of comprehensive decision-making in complex clinical scenarios.
In recent years, multimodal large models have demonstrated excellent cross-modal understanding and knowledge transfer capabilities in natural images and general vision-language tasks, providing a new paradigm for medical AI. However, direct application in medical scenarios still faces challenges: first, the medical semantic system differs significantly from general language models, hindering the accurate representation of disease characteristics and imaging details; second, the complex morphology of lesions and uneven sample distribution in medical data increase the difficulty of model generalization; third, clinical data involves privacy, so data security and ethical compliance serve as prerequisites for research.
The research on medical multimodal large models aims to integrate multi-source heterogeneous medical data, establish a unified semantic representation and reasoning mechanism, and realize full-process intelligent analysis including disease identification and lesion localization. This approach can not only improve the efficiency and accuracy of clinical diagnosis but also provide clinicians with interpretable and traceable auxiliary decision support, boasting broad application prospects.
Based on the hospital's clinical data resources and the research team's algorithmic foundation, this study intends to construct a multimodal large model system for medical imaging diagnosis, enabling closed-loop intelligent analysis from multimodal information fusion to diagnostic report generation. The research will strictly adhere to medical ethical standards, protect patients' right to information, right to privacy, and data security. Before the official launch of the project, ethical review must be passed, and relevant regulations shall be followed to ensure the unity of scientific research and ethics, laying a compliant foundation for subsequent clinical validation and promotion.
Detailed description
With the continuous accumulation of medical imaging, electronic health records (EHRs), and multimodal clinical data, how to efficiently leverage multi-source medical information to achieve precise diagnosis and intelligent decision-making has become a core direction in the development of medical artificial intelligence (AI). Although traditional unimodal algorithms (e.g., models based solely on CT, MRI, or ultrasound images) have yielded certain results in specific tasks, their inability to model semantic correlations among imaging, textual, and laboratory data often leads to insufficient stability and limited interpretability of diagnostic outcomes, making it difficult to meet the comprehensive decision-making needs of complex clinical scenarios.
In recent years, multimodal large language models (MLLMs) have demonstrated remarkable cross-modal understanding and knowledge transfer capabilities in natural image processing and general vision-language tasks, providing a new technical paradigm for medical AI. However, the direct application of such models in medical scenarios still faces multiple challenges: first, there are significant discrepancies between the medical semantic system and general language models, hindering the accurate representation of disease characteristics and imaging details; second, the complex morphology of lesions and imbalanced sample distribution in medical data increase the difficulty of model generalization; third, clinical data involves privacy-sensitive information, making data security and ethical compliance a prerequisite for research.
Research on medical multimodal large models aims to comprehensively utilize multi-source heterogeneous data-such as medical imaging (e.g., CT, MRI, X-ray), EHRs, and laboratory reports-to establish a unified semantic representation and reasoning mechanism, enabling end-to-end intelligent analysis including disease identification, lesion localization, report generation, and disease progression prediction. This research direction not only helps improve the efficiency and accuracy of clinical diagnosis but also provides clinicians with interpretable and traceable auxiliary decision support, boasting broad prospects for clinical application.
Based on the hospital's abundant clinical data resources and the research team's algorithm development foundation, this study intends to construct a multimodal large model system for medical imaging diagnosis, realizing a closed-loop intelligent analysis pipeline from multimodal information fusion to diagnostic report generation.
During the research implementation, strict adherence to medical ethical standards will be followed to fully protect patients' right to informed consent, privacy, and data security. To ensure the scientificity and compliance of the research design, this project must pass ethical review prior to its official launch. In accordance with relevant regulations including the Declaration of Helsinki, International Ethical Guidelines for Health-related Research Involving Humans, and Ethical Review Measures for Life Science and Medical Research Involving Humans, we will achieve the organic integration of scientific research and ethical principles, laying a compliant foundation for subsequent clinical validation and application promotion.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 4 Weeks
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group1:This study enrolled adult patients aged ≥ 18 years who underwent imaging examinations (includ | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- he study population was composed of adult patients who met the preset inclusion criteria and were free of any exclusion criteria. In terms of inclusion requirements, eligible patients were aged 18 years or older and had received imaging examinations including computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound at the hospital during the study period. The examination items were required to target the hepatobiliary and pancreatic system, which was the focus of the research. All enrolled patients needed to be equipped with at least complete imaging data and radiological diagnostic reports, with relevant medical record information serving as supplementary modalities. In addition, written informed consent must be obtained from the patients themselves or their legal representatives, who agreed that the de-identified data of the patients could be used for the validation of scientific research models. Finally, all case data of the selected patients had to pass strict qual
Key inclusion criteria (5 listed)
- Adult patients aged ≥ 18 years.
- Patients who underwent imaging examinations (CT, MRI, ultrasound, etc.) at this hospital during the study period.
- The examination items are consistent with the disease types or systems focused on by the study (hepatobiliary and pancreatic system).
- Possess at least complete imaging data, radiological diagnostic reports, with relevant medical record information as supplementary modalities.
- Patients and their legal representatives have signed an informed consent form, agreeing to the use of their de-identified data for scientific research model validation.
Key exclusion criteria (5 listed)
- Patients who refuse to sign the informed consent form.
- Cases with unassessable images due to severe motion artifacts, incomplete scanning, or equipment abnormalities.
- Cases with severe deficiency of clinical data or failure to match with imaging data.
- Cases with special pathological conditions or post-operative status (e.g., extensive resection, significant structural changes after radiotherapy) that affect the consistency of model analysis.
- Data samples with privacy protection or legal risks.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Disease Diagnosis Task
Time frame: From enrollment to the end of diagnosis at 3 days
The primary outcome is the accuracy and reliability of the multimodal imaging diagnostic model in identifying and classifying target diseases compared with the gold standard of clinical diagnosis by senior radiologists.
Lesion Localization and Segmentation Task
Time frame: from enrollment to end of diagnosis up to 3 days
It includes the model's performance in precise localization, contour segmentation and quantitative measurement of lesions, assessed by Dice similarity coefficient, IoU and localization error.
Diagnostic Report Generation Task
Time frame: from enrollment to end of diagnosis up to 3 days
It evaluates the clinical validity, completeness, consistency and readability of automatically generated radiology reports relative to manual reports.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Mar 2026.
- Individual participant dataSharing planned: No. The decision not to share individual participant data (IPD) from this study is primarily based on ethical, privacy protection, and legal compliance considerations, as well as the inherent characteristics of the research data involved. First and foremost, the core data of this study includes de-identified imaging materials, radiological diagnostic reports, and associated medical records of patients, which are closely linked to personal health information. Despite the implementation of de-identification procedures during data collection and collation, there remains a potential risk of re-identifying individual participants if the data are shared without strict restrictions. Such a risk would violate the stipulations of relevant privacy protection laws and regulations, as well as the informed consent signed by the patients and their legal representatives. It should be emphasized that the informed consent clearly specifies that the de-identified data of participants is only used for the in
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Liver Diseases and Gallbladder Diseases, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 3 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact ding Yuan, Doctor (+86 18858101960, dingyuan@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Disease Diagnosis Task (time frame: From enrollment to the end of diagnosis at 3 days). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Mar 2026.