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.

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.

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

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