Research on Construction and Verification of Multimodal Medical Imaging Large Model

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.

Inclusion criteria

• 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.

Exclusion criteria

• 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.