Multimodal Large Language Models in Healthcare: Current Applications and Validation Approaches

June 28, 2025 • Binesh K Sadanandan

2025   ·   multimodal-llms   medical-imaging   ehr-analysis   clinical-ai   validation-datasets     ·   research   healthcare-ai  

Multimodal Large Language Models (LLMs) can read images, text, time series, and audio all at once. They can spot findings in scans, summarize clinical notes, and track patient data over time. This review:

  1. Explains why multimodal models matter
  2. Describes leading models in medical imaging
  3. Covers models for electronic health records
  4. Lists key clinical validation datasets
  5. Notes deployment challenges
  6. Suggests future directions

1. Why Multimodal AI Matters in Healthcare

Healthcare data comes in many forms

  • Scans and slides: X rays, MRIs, CT scans, histology images
  • Clinical notes: admission and discharge summaries, progress notes
  • Time series: vital signs, lab trends, ECG waveforms
  • Audio: heart and lung sounds, patient interviews

A single model that handles all these inputs can

  • Detect subtle abnormalities in images
  • Summarize long reports in plain language
  • Integrate chart trends with clinical context
  • Support diagnosis and treatment planning

2. Vision-Language Models for Medical Imaging

Multimodal models let us ask questions about images and get text answers. Here are four examples.

2.1 LLaVA-Med 1.5

  • Vision encoder: CLIP ViT-L/336px
  • Connector: MLP to map image features into text space
  • Language model: Vicuna v1.5 (13 B)
  • Data: 200 K image–text pairs from PubMed Central, GPT-4 synthetic instructions
  • Tasks: radiology VQA, visual report generation, pathology Q&A
  • Performance: matches or beats benchmarks on VQA-Radiology and pathology tests

2.2 Visual Med-Alpaca

  • Base: LLaMA-7 B with LoRA adapters
  • Pipeline: type classifier routes input, Med-GIT and DePlot experts process images, LLaMA core generates text
  • Data: 54 K Q&A pairs from BigBIO and ROCO radiology sets, GPT-3.5–generated and human-filtered prompts
  • Notes: research use only, not FDA approved
  • Results: state-of-the-art accuracy on image QA benchmarks

2.3 CheXagent

  • Image encoder: SigLIP-Large (24 layers, 512 px)
  • Projector: MLP mapping 1 024 → 2 560 dims
  • Decoder: Phi-2.7 B trained on medical and scientific text
  • Training: 1 M+ chest X ray–report pairs, 2.7 B tokens from clinical notes and articles
  • Applications: draft radiology reports, detect abnormalities, explain findings

2.4 MedGemma-4B-IT

  • Architecture
    • Decoder-only transformer (Gemma 3 base) with 4 B parameters
    • SigLIP image encoder pre-trained on de-identified chest X rays, dermatology, ophthalmology, histopathology
  • Inputs and outputs
    • Up to 128 K text tokens plus images (896 × 896 px, 256 tokens each)
    • Generates text: reports, answers, summaries
  • Technical specs
    • Context length: at least 128 K tokens
    • Attention: grouped-query attention
    • Release: July 9, 2025 (v1.0.1)

  • Key performance

    Task Base Gemma 3 4B MedGemma 4B-IT
    MIMIC-CXR macro F1 (top 5) 81.2 88.9
    CheXpert macro F1 (top 5) 32.6 48.1
    CXR14 macro F1 (3 conds) 32.0 50.1
    SLAKE VQA token F1 40.2 72.3
    PathMCQA histopath accuracy 37.1 69.8
    EyePACS fundus accuracy 14.4 64.9
  • Availability
    • Hosted on Hugging Face under Health AI Developer Foundations license
    • Quick-start and fine-tuning notebooks on GitHub

3. Language Models for Electronic Health Records

Models can also read and reason over clinical text and signals.

3.1 GatorTron

  • Size: 110 M to 8.9 B parameters
  • Corpus: 82 B words of de-identified clinical text
  • Tasks: concept extraction, relation extraction, inference, question answering
  • Finding: larger models and more data boost all clinical NLP tasks

3.2 Few-Shot Health Learners

  • Base: PaLM-24 B pretrained on 780 B tokens
  • Adaptation: few-shot fine-tuning on time series (ECG, vitals)
  • Uses: arrhythmia detection, activity recognition, calorie and stress estimation
  • Insight: large LLMs can ground numeric health data with minimal examples

4. Validation Datasets for Clinical AI

Clinical deployment needs rigorous testing on real-world cases:

  1. NEJM Clinicopathologic Cases
    • 143 puzzles (2021–2024), scored by Bond Score (0–5) and Likert (0–2)
  2. NEJM Healer Series
    • 20 cases, four stages: triage, exam, tests, management; R-IDEA rubric (0–10)
  3. Grey Matters Management
    • 5 scenarios, 100-point rubric; compares GPT-4 vs physicians with and without AI
  4. MIMIC-IV-Ext Clinical Decision Making
    • 2 400 ED visits for abdominal pain; diagnoses: appendicitis, cholecystitis, diverticulitis, pancreatitis
  5. Probabilistic Reasoning Challenges
    • Bayesian inference tasks with lab results; evaluate error in probability estimates

5. Deployment Considerations

Safe clinical use requires:

  • Privacy
    • De-identify data, encrypt records
  • Generalization
    • Test on diverse hospitals and patient groups
  • Explainability
    • Provide attention maps, saliency scores, counterfactuals
  • Regulation
    • Define liability, follow FDA and CE guidelines

6. Next Steps and Future Directions

  1. Expand modalities: add genomics, proteomics, wearable data
  2. Real-time AI: integrate into EHRs for live decision support
  3. Personalization: fine-tune on individual patient histories
  4. Unified benchmarks: cover performance, safety, fairness

Multimodal LLMs can revolutionize healthcare, but careful validation and deployment are key to patient safety and trust.



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