Clinical Research Directory

Integrating multimodal medical data (electronic medical records, medical images and reports, laboratory results, genetic information) with medical guidelines, expert consensus, clinical databases, medical literature, encyclopedias, patents, and doctor-patient dialogue data. Datasets are constructed in phases: pre-training general knowledge datasets (guidelines, textbooks, literature, historical records) and fine-tuning disease-specific datasets (doctor-patient dialogues, disease-specific records, treatment plans, follow-up records). Techniques include data cleaning, standardization, transformation, annotation, and augmentation. The platform adopts a human-machine collaboration strategy to reduce data annotation costs, combining medical experts' professional knowledge with AI capabilities.

Extracting multimodal knowledge graphs from guidelines, consensus, and literature using the QLora training framework. The process involves medical knowledge modeling (defining entities, relations, attributes), entity recognition (disease names, drug names), relation extraction (disease-symptom, drug-treatment relationships), attribute extraction (incidence, dosage), and knowledge fusion and completion to build a tens-of-millions-level knowledge graph. The construction incorporates multimodal data integration (text, images, audio) through image recognition and speech recognition technologies.

Developing fine-tuning techniques for different disease areas using the ChatGLM large model, with integration of expert feedback. Pre-training uses large-scale medical datasets with language modeling tasks. Fine-tuning incorporates physician annotation, data augmentation (multi-task learning, adversarial training), and prompt-tuning to generate disease-specific responses. Deployment applies quantization to reduce model size and improve inference speed. A quality evaluation system is developed as a standardized method for measuring model performance, incorporating customized methods tailored to specific diseases and drawing on existing frameworks such as Ragas and CMB.

Building an intelligent medical history collection system using voice interaction, natural language processing, and large model technologies. Speech recognition and NLP techniques collect patient information through multi-round dialogue with strategies including questioning, paraphrasing, feeling reflection, and self-disclosure. Physicians supervise and validate the model through Reinforcement Learning from Human Feedback to improve collection efficiency. After collection, the model extracts key information (chief complaint, history of present illness, past medical history, personal history, allergy history) and uses Retrieval-Augmented Generation (RAG) combined with guidelines for preliminary triage.

Building an intelligent clinical auxiliary diagnosis system with multi-source data fusion integrating heterogeneous medical records, laboratory reports, and imaging text reports using OCR and natural language processing (NLP). Using chain-of-thought and reasoning capabilities of large language models combined with knowledge graph retrieval to generate evidence-based diagnostic recommendations. Physicians provide feedback through a data annotation interface to fine-tune the system, with customization settings to meet individual physician needs.

Building a medical record generation system enabling automatic entry of patient information and automatic medical record generation using large language models for entity recognition, text classification, and semantic understanding. The system generates records compliant with writing standards including chief complaint, history of present illness, past medical history, personal history, marital and reproductive history, family history, and auxiliary examinations. A quality control system automatically detects generated records and, when core fields are missing, alerts physicians through highlighting and submission blocking, prompting them to make necessary additions and modifications.

Providing a solution for consultation audio data collection and processing to support intelligent history collection, rapid triage, and clinical auxiliary diagnosis. The collected audio is converted to accurate textual content with preliminary analysis to identify key medical information (symptoms, medications, treatment processes). The study addresses speech recognition challenges including specialized terminology, dialects, accents, and elderly speech characteristics, using refined speech processing algorithms and deep learning approaches. Strict data protection measures (encryption, anonymization) are applied throughout collection, transmission, storage, and processing to ensure patient data security.

Validating the generalizability of the five intelligent diagnosis and treatment systems (gastric cancer, chronic gastritis, GERD, coronary artery disease, stroke) by transferring the fine-tuned systems from the training center to other centers for external cross-validation. Feasibility and accuracy are evaluated by comparing information collected from large model-patient interactions and physician-patient interactions across different center environments.