MLLM4BioMed: A Review and Guide to Multimodal Large Language Models in Biomedicine

MLLM4BioMed is a resource repository for biomedical multimodal large language models (MLLMs) maintained by the Natural Language Processing team at the U.S. National Center for Biotechnology Information (NCBI). It systematically organizes the application status, technical key points, and deployment guidelines of MLLMs in the biomedical and healthcare fields. This project bridges the knowledge gap between academic research and practical applications, helping users safely and effectively apply multimodal AI technologies to healthcare scenarios.

With the development of large language model technology, multimodal capabilities have become an important feature of the next generation of AI. In the biomedical field, multimodal LLMs can process various types of modal information such as text, images, genomic data, and clinical records, providing intelligent solutions for disease diagnosis, drug development, medical education, and clinical decision support. The MLLM4BioMed project, initiated by the NCBI NLP team, aims to provide a comprehensive review and practical guide for the deployment of multimodal LLMs in the biomedical field.

Modal Alignment and Fusion

Mainstream solutions include encoder projection, unified tokenization, and cross-modal attention mechanisms to achieve effective integration of data from different modalities.

Domain Adaptation Training

General models need to optimize their performance on medical tasks through continuous pre-training (using medical multimodal corpora), instruction fine-tuning (for medical Q&A/report generation), and multi-task learning.

Hallucination Issues and Reliability

System reliability is enhanced through retrieval-augmented generation (RAG) anchored to trusted knowledge bases, multimodal fact-checking tools, and human-machine collaborative workflows.

• Medical Image Report Generation: Automatically analyze radiological images to generate structured reports, and improve description accuracy by combining clinical context.
• Pathology-Assisted Diagnosis: Scan whole-slide images to identify abnormal areas and provide differential diagnosis suggestions based on medical history.
• Drug-Target Interaction Prediction: Integrate molecular structure, protein data, and literature knowledge to accelerate new drug discovery.
• Clinical Decision Support: Analyze multi-dimensional patient data to assist in drug interaction detection, anomaly warning, and treatment plan recommendation.

• Data Privacy and Security: Use federated learning, differential privacy, and homomorphic encryption to protect patient privacy.
• Regulatory Compliance: Follow regulations such as FDA SaMD and EU MDR, and provide compliance checklists.
• Fairness and Bias Mitigation: Conduct fairness audits during model development and evaluation to ensure consistent performance across different populations.
• Interpretability: Use attention visualization and Concept Activation Vectors (CAV) to enhance decision transparency.

MLLM4BioMed is open-source and hosted on GitHub, providing:

• Model review documents (covering mainstream multimodal medical LLMs such as Med-PaLM M, LLaVA-Med, etc.)
• Benchmark testing guidelines (standard datasets and evaluation metrics)
• Deployment tutorials (from environment configuration to production deployment)
• Case studies (practical application experiences)

The community can participate in discussions, report issues, or contribute resources/tools via GitHub Issues.

Future directions include:

• Real-time Multimodal Interaction: Process real-time data such as surgical videos and sensors
• Personalized Medicine: Provide precise recommendations by combining genomic data, lifestyle, and clinical records
• Scientific Discovery: Uncover cross-modal insights (e.g., disease biomarkers)
• Global Health Equity: Promote applications in resource-poor areas to narrow the healthcare gap

The project will be continuously updated to track field progress and provide reliable resources.