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.
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 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.
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
Other
Prospective
Not listed
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.
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.
Diagnostic Report Generation Task
It evaluates the clinical validity, completeness, consistency and readability of automatically generated radiology reports relative to manual reports.
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