ReviewNational science review2024
A survey on multimodal large language models.
Review in National science review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
92 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Reporting guideline for Chatbot Health Advice studies: the CHART statement.BMC medicine · 2025Guideline
- Harnessing large vision and language models in agriculture: a review.Frontiers in plant science · 2025Pooled it
- Article
- Dual-brain fusion: Bidirectional empowerment between human brain and brain-inspired systems.Fundamental research · 2026Review
- Discordance Between Textual Reasoning and Visual Interpretation in Large Language Models for Low Back Pain: Cross-Sectional Quantitative Evaluation and Exploratory Multimodal Stress Test.JMIR medical informatics · 2026Article
- Multiagent Data Mining for Enhanced Machine Learning Prediction of Delayed Fluorescence Materials.ACS omega · 2026Article
- Vision-Based Digital Twin and AI Agent Framework for Low-Cost, Explainable Indoor Building Inspection and Safety Assessment.Sensors (Basel, Switzerland) · 2026Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Performance of multimodal large language models on image-based surgical anatomy, anatomical pathology, and radiology questions.Anatomical sciences education · 2026Article
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- A CNN-based liver ultrasound plane recognition system with multimodal large language model-generated interpretable feedback.Abdominal radiology (New York) · 2026Article
- Large Language Models in Sensor-Driven Control Systems: Architectures, Challenges, and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- BoneCoT: multicentre validation of a whole-body skeleton foundation model for bone metastases guided by clinician-derived chain of thought.Nature biomedical engineering · 2026Article
- Review
- LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.ACM transactions on computing for healthcare · 2026Article
- Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.NPJ digital medicine · 2026Article
- Optimizing a Multimodal Large Language Model for Ultrasound-Based Thyroid Nodule Malignancy Classification: A Comparative Study of Few-Shot Learning, Prompt Engineering, and Fine-Tuning.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Integrating speech biomarkers and large language models for adolescent suicide risk detection with mobile application for real-world evaluation.Cell reports. Medicine · 2026Article
- Ceramic art design generation and aesthetic quality evaluation based on multimodal large language models and diffusion probabilistic framework.Scientific reports · 2026Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the multimodal large language model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful large language models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of the MLLM, such as writing stories based on images and optical character recognition-free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence. To this end, both academia and industry have endeavored to develop MLLMs that can compete with or even outperform GPT-4V, pushing the limit of research at a surprising speed. In this paper, we aim to trace and summarize the recent progress of MLLMs. First, we present the basic formulation of the MLLM and delineate its related concepts, including architecture, training strategy and data, as well as evaluation. Then, we introduce research topics about how MLLMs can be extended to support more granularity, modalities, languages and scenarios. We continue with multimodal hallucination and extended techniques, including multimodal in-context learning, multimodal chain of thought and LLM-aided visual reasoning. To conclude the paper, we discuss existing challenges and point out promising research directions.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.