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Research on Construction and Verification of Multimodal Medical Imaging Large Model

NCT07445152 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2026-03-03

No results posted yet for this study

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). 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.

Conditions

Sponsors & Collaborators

  • Second Affiliated Hospital, School of Medicine, Zhejiang University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-11-15
Primary Completion
2027-11-15
Completion
2027-11-15

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07445152 on ClinicalTrials.gov