MedVLM: Medical Vision–Language Model for Consumer Devices
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) has enabled significant advancements in healthcare by supporting complex medical tasks through multimodal data processing. However, existing models often lack the adaptability required for diverse medical applications and are limited by their large size, hindering real-time deployment on consumer and edge devices. This article presents MedVLM, a novel vision–language model optimized for medical applications, such as visual question–answering (VQA) and medical report generation. MedVLM integrates the Florence-2 visual model with the LLaMA-2 language model using low-rank adaptation, reducing the number of trainable parameters to support efficient, real-time analysis across various imaging modalities, including X-rays, CT scans, and MRIs. Our evaluation includes extensive benchmarking against both specialized (Open-Flamingo, MedVInT, and Med-Flamingo) and generalist (Qwen-VL, PaLM-E) models, with results showing MedVLM’s superior performance in diagnostic accuracy and VQA tasks, achieving 0.51% accuracy on the RadVQA dataset. We also validate MedVLM’s outputs through collaboration with radiologists, who rated 74% of its generated medical reports as high quality. This work bridges the gap between GenAI advancements and practical radiological needs, providing a versatile tool that can streamline workflows and enhance diagnostic accuracy across various clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".