WoundQwen: Multistage MLLM for Wound Diagnosis

• WoundQwen is a three-stage multimodal framework that integrates image analysis with structured clinical cues to generate actionable surgical wound reports.
• It decomposes diagnosis into wound-characteristic extraction, infection risk and urgency prediction, leveraging intermediate outputs for enhanced reasoning.
• The framework, validated on SurgWound-Bench, outperforms baselines in both VQA and report generation tasks using metrics like ACC, BLEU, and ROUGE.

WoundQwen is a three-stage multimodal LLM (MLLM) framework for surgical wound diagnosis and patient-facing wound care guidance introduced together with SurgWound-Bench, a benchmark built on the open SurgWound dataset. Its central design premise is that surgical wound assessment should not be reduced to direct image classification alone: the framework first predicts clinically salient wound characteristics, then uses those predictions as additional knowledge inputs for infection-risk and urgency assessment, and finally synthesizes a comprehensive report. In the originating formulation, WoundQwen is explicitly positioned against a prior landscape dominated by private datasets, image-only CNN classifiers, and systems that cannot incorporate richer clinical context such as wound location or produce understandable guidance (Xu et al., 21 Aug 2025).

1. Clinical task and conceptual scope

WoundQwen was proposed for surgical wound diagnosis, with emphasis on infection risk, urgency guidance, and report generation from surgical images plus structured wound attributes. The motivating clinical condition is surgical site infection (SSI), described as one of the most common and costly healthcare-associated infections; the paper frames surgical wound care as a setting in which earlier and more structured AI assistance could improve screening and downstream intervention decisions (Xu et al., 21 Aug 2025).

The framework separates two decision outputs that are clinically linked but not identical. Infection Risk is modeled with the labels Low, Medium, and High, whereas Urgency Level is modeled with Home Care, Clinic Visit, and Emergency Care. The paper notes that these outputs are strongly correlated, typically with low risk corresponding to home care, medium risk to clinic visit, and high risk to emergency care, while also noting that a small number of examples deviate slightly on surgeon judgment. This separation is important because it formalizes both etiologic assessment and actionable guidance rather than collapsing the task into a single label (Xu et al., 21 Aug 2025).

A common simplification is to treat WoundQwen as a monolithic image-to-text model. In the published formulation, it is instead a staged diagnostic system in which intermediate wound-characteristic predictions are retained and reused. This design makes the model closer to a structured reasoning pipeline than to a generic visual captioner or free-form medical chatbot.

2. SurgWound and SurgWound-Bench

WoundQwen is built on SurgWound, described as the first open-source dataset for diverse surgical wound analysis. The data were collected from publicly available social media and online platforms, specifically RedNote, Twitter, Facebook, Instagram, and Reddit, using domain hashtags, keywords, and posts from surgeons’ accounts. Filtering proceeded in two steps: GPT-4o first screened images for clear surgical wound content and quality, and then three surgeons manually verified the retained images and removed non-surgical or poor-quality cases (Xu et al., 21 Aug 2025).

The final dataset contains 686 images in the main description, although the abstract reports 697. The detailed dataset section and tables use 686, split into 480 training, 69 validation, and 173 test images in a 7:1:2 ratio. Each image is annotated by three professional surgeons with 8 structured clinical labels:

• Location
• Healing Status
• Closure Method
• Exudate Type
• Erythema Presence
• Edema Presence
• Infection Risk
• Urgency Level

The label spaces are explicitly defined. Location includes Abdomen, Ankle, Facial region, Manus, Patella, Cervical region, Other, Uncertain. Healing Status includes Healed and Not Healed. Closure Method includes Invisible, Staples, Sutures, Adhesives, Uncertain. Exudate Type includes Non-existent, Purulent, Serous, Sanguineous, Seropurulent, Uncertain. Erythema and Edema each include Non-existent, Existent, Uncertain. Infection Risk includes Low, Medium, High, and Urgency Level includes Home Care, Clinic Visit, Emergency Care. The annotation scheme permits Uncertain when the value cannot be determined from the image alone, and Other is defined as a location that is identifiable but not among common categories (Xu et al., 21 Aug 2025).

The class distribution is highly imbalanced: most cases are Low risk and Home Care, and most wounds are Healed or have No exudate. The paper therefore analyzes statistical relationships between wound characteristics and infection risk using a chi-square test of independence, Cramér’s V, and relative risk (RR). All tested attributes have $p < 0.05$, indicating association with infection risk; the strongest associations are for Healing Status, Exudate Type, Erythema, and Edema, while Location and Closure Method are weaker. Using RR, the authors identify Not Healed, Erythema, Edema, and exudates such as Serous, Sanguineous, Purulent, and Seropurulent as elevated-risk factors (Xu et al., 21 Aug 2025).

On top of SurgWound, the authors define SurgWound-Bench, which contains two tasks. SurgWound-VQA is a multiple-choice benchmark derived from the annotations, excluding any attribute marked Uncertain; it evaluates seven sub-tasks: Healing Status, Closure Method, Exudate Type, Erythema, Edema, Infection Risk, and Urgency Level. SurgWound-Report uses test-set images with reference reports constructed through a two-step pipeline in which GPT-4o first generates a structured draft from the image and annotations, after which surgeons manually revise the draft for accuracy and consistency. VQA is evaluated with Accuracy, Precision, Recall, F1, Micro-F1, Macro-F1, and Weighted-F1, while report generation uses BLEU-1, BLEU-2, BLEU-3, ROUGE-1, ROUGE-2, ROUGE-L, and BERTScore (Xu et al., 21 Aug 2025).

3. Three-stage architecture

The core contribution of WoundQwen is a staged MLLM architecture that decomposes diagnosis into wound-characteristic extraction, diagnostic outcome prediction, and report generation. The design is intended to mirror clinical reasoning: first identify local wound findings, then interpret their implications for risk and intervention, and only then compose narrative guidance (Xu et al., 21 Aug 2025).

Stage	Inputs	Outputs
Stage 1	Image	Healing Status, Closure Method, Exudate Type, Erythema, Edema
Stage 2	Image, Location, Stage-1 predictions	Infection Risk, Urgency Level
Stage 3	Image, Location, Stage-1 and Stage-2 outputs	Comprehensive wound report

In Stage 1, WoundQwen trains five separate Qwen-based MLLMs to predict five wound characteristics from the image: Healing Status, Closure Method, Exudate Type, Erythema, and Edema. Location is not predicted; it is treated as known context. The predicted characteristic outputs are denoted as

$\{\hat{y}_i\}_{i=1}^{5}.$

In Stage 2, two additional models consume the image, the known Location, and the Stage-1 predictions. The paper formalizes the mechanism as

$$\hat{y}_{\text{risk}} = \mathrm{WoundQwen\_risk}\bigl(\text{Image},\; \text{Location},\; \{\hat{y}_i\}_{i=1}^{5}\bigr)$$

and

$$\hat{y}_{\text{urgency}} = \mathrm{WoundQwen\_urgency}\bigl(\text{Image},\; \text{Location},\; \{\hat{y}_i\}_{i=1}^{5}\bigr).$$

This is the framework’s defining “predictions as additional knowledge inputs” mechanism. Rather than discarding intermediate outputs, WoundQwen injects them as structured evidence for downstream reasoning (Xu et al., 21 Aug 2025).

In Stage 3, WoundQwen_report generates a comprehensive wound report conditioned on the raw image, wound characteristics, infection risk, urgency level, and known location. The paper describes this as a means of producing a clinically grounded narrative summary rather than a free-form hallucinated description. The resulting output is intended to resemble a clinical note or patient instruction more than a generic caption (Xu et al., 21 Aug 2025).

4. Model implementation and training regime

The Stage-1 models are based on Qwen2.5-VL-7B, initialized from HuatuoGPT-Vision-7B weights and fine-tuned with LoRA supervised fine-tuning (SFT). The vision encoder is frozen, and LoRA adapters are applied to the LLM layers, specifically to the attention projections $q\_proj, k\_proj, v\_proj, o\_proj$ and MLP projections $gate\_proj, up\_proj, down\_proj$. Training uses VQA-style instruction pairs such as “What is the healing status of this surgical wound?” (Xu et al., 21 Aug 2025).

Stage 2 also uses LoRA-based SFT, but with prompts augmented by wound-characteristic descriptions. The architectural point is not merely parameter-efficient fine-tuning; it is the explicit conditioning of risk and urgency prediction on a structured latent clinical state inferred in Stage 1. The authors report that these intermediate cues are clinically meaningful because wound healing status, exudate, erythema, and edema have statistically supported associations with infection risk in the dataset analysis (Xu et al., 21 Aug 2025).

Stage 3 is trained on the surgeon-refined image-report pairs from SurgWound-Bench. Its prompt concatenates the outputs from Stages 1 and 2 together with known location, creating a report-generation setting in which narrative synthesis is anchored by structured diagnostic context. This training design is explicitly motivated by reducing hallucination relative to direct image-to-text prompting (Xu et al., 21 Aug 2025).

A frequent misunderstanding is that the intermediate predictions are only auxiliary supervision. The paper’s formulation makes them central to the pipeline: they function as explicit, reusable clinical evidence. This suggests that WoundQwen should be interpreted as a structured multimodal reasoning system rather than a conventional single-pass classifier.

5. Quantitative performance and ablation evidence

Across the benchmark, WoundQwen is reported to outperform all compared baselines, including proprietary models GPT-4o, Claude-3.5, Gemini-1.5, Gemini-2.0, and open models such as HuatuoGPT-34B, InternVL3-78B, and Qwen2.5-VL-72B. The gains are reported for both the VQA tasks and the report-generation task, indicating that the framework improves not only categorical prediction but also report fidelity (Xu et al., 21 Aug 2025).

For VQA, the paper highlights several representative results. On Exudate Type, WoundQwen reaches ACC 88.80% and weighted-F1 0.884. On Infection Risk, it reaches ACC 83.21% and weighted-F1 0.845. On Urgency Level, it reaches ACC 82.35% and weighted-F1 0.822. The infection-risk classwise F1 values are 0.913 for Low, 0.522 for Medium, and 0.444 for High. The paper emphasizes that this is much stronger than the baselines on rare classes, many of which fail entirely on the high-risk class (Xu et al., 21 Aug 2025).

For report generation, WoundQwen obtains BLEU-1 0.457, BLEU-2 0.300, BLEU-3 0.212, ROUGE-1 0.612, ROUGE-2 0.279, ROUGE-L 0.418, and BERTScore 0.917. The next-best models are described as far behind on BLEU and ROUGE, indicating materially better n-gram overlap and content coverage, while WoundQwen also achieves the highest semantic similarity (Xu et al., 21 Aug 2025).

The ablation findings are as important as the headline scores. First, HuatuoGPT-7B + LoRA SFT improves over the base model but remains notably worse than the full three-stage framework. Second, feeding ground-truth clinical cues instead of Stage-1 predictions improves Stage-2 performance, confirming that the intermediate attributes are informative and that Stage-1 error propagates downstream. Third, even general models such as GPT-4o and Claude-3.5 improve when given ground-truth wound cues, which indicates that the structured attributes themselves have diagnostic value independent of the particular backbone. The paper therefore interprets the performance gap between predicted cues and ground-truth cues as motivation for the staged design: iterative reasoning helps, but its effectiveness depends on accurate intermediate feature extraction (Xu et al., 21 Aug 2025).

6. Position within wound-AI research and principal limitations

WoundQwen belongs to a broader transition in wound AI from narrow image-only classifiers toward multimodal systems that integrate image evidence, structured clinical cues, and downstream communication tasks. Earlier wound models emphasized image classification, including multi-task CNN classification of five wound conditions from color images under real-world acquisition variability (Huang et al., 2023). Subsequent multimodal systems showed that adding location metadata to image features can materially improve wound-type classification, as in an Xception + GMRNN architecture for diabetic, pressure, surgical, and venous ulcers (Mousa et al., 12 May 2025). Other work extends multimodal reasoning toward hospitalization-risk prediction by combining image-derived wound variables with clinician-filled variables in a two-stage pipeline using Xception and LightGBM (Oota et al., 10 Feb 2026). Within wound-language tasks, a related but distinct direction is free-text wound care VQA for patient queries paired with one or two wound images, where retrieval and metadata guidance are used to generate concise clinical responses in the MEDIQA-WV 2025 setting (Durgapraveen et al., 13 Nov 2025).

Against that background, WoundQwen is distinguished by three features that co-occur in one framework: an open surgical-wound dataset, a benchmark spanning both VQA and report generation, and a three-stage MLLM pipeline whose central mechanism is the injection of structured intermediate predictions as knowledge inputs. This suggests that its main contribution is not only higher benchmark performance, but also a more explicit operationalization of staged clinical reasoning.

The paper is nonetheless explicit about constraints. The dataset is highly imbalanced; rare classes remain difficult. The abstract and main text report different dataset sizes (697 versus 686), which is a documentation inconsistency rather than a modeling claim. More substantively, the ablation results show that Stage-2 performance improves when ground-truth cues replace predicted ones, so the framework remains sensitive to upstream prediction quality. A second misconception is that the model is fully image-derived; in fact, Location is treated as known context rather than predicted. A plausible implication is that deployment quality will depend not only on image interpretation but also on the availability and correctness of structured metadata. The paper’s own summary of significance is therefore measured: WoundQwen is presented as a more interpretable and clinically useful wound-analysis system because it progresses from detailed wound-characteristic extraction to diagnostic judgment and finally to report generation, rather than because it resolves all uncertainties in automated wound assessment (Xu et al., 21 Aug 2025).