Patient-Centered Dermatological VLM

• Patient-Centered Dermatological VLM is a framework that integrates richly annotated image–text datasets, clinical ontologies, and patient metadata to support transparent and equitable diagnostics.
• It employs hybrid neural architectures, concept bottlenecks, prompt engineering, and chain-of-thought reasoning to generate detailed, interactive, and clinically interpretable outputs.
• PC-DVLM advances diagnostic precision and fairness by utilizing equity-aware learning, synthetic data balancing, and personalized prompt techniques for diverse patient subgroups.

Patient-centered dermatological vision-language modeling (PC-DVLM) defines a research framework that integrates multimodal neural architectures, domain ontologies, extensive image–text corpora, and diagnostic reasoning algorithms to produce clinically interpretable, context-aware, and equitable dermatology diagnostics, explanations, and recommendations. PC-DVLM systems explicitly model the granularity of dermatological presentations, patient metadata, diagnostic workflow, and the linguistic forms required for useful communication with both clinicians and lay users. This field advances beyond traditional black-box image classifiers by introducing structured conceptual bottlenecks, chain-of-thought reasoning, equity-aware learning, and interactive VQA pipelines tailored to diverse patient subgroups.

1. Multimodal Datasets, Ontologies, and Patient Metadata Integration

The acceleration of PC-DVLM research is driven by large-scale, richly annotated dermato-vision-language datasets such as Derm1M (1,029,761 image–text pairs, 390 ontologized diagnoses, 130 clinical concepts) (Yan et al., 19 Mar 2025), MM-Skin (11,039 images, expert captions, 27,412 VQA samples, three imaging modalities) (Zeng et al., 9 May 2025), DermoInstruct (211,243 images, 772,675 instruction trajectories, 903 raw labels unified to 325 subclasses) (Ru et al., 5 Jan 2026), and patient-centric resources like DermaVQA-DAS (bilingual assessment schema with 36 high-level and 27 fine-grained questions; >7,400 segmentation masks from teledermatology encounters) (Yim et al., 30 Dec 2025).

These corpora embed medical history, body site, skin tone (often Fitzpatrick I–VI), demographic tags, symptoms, and prior treatments directly into textual supervision. The structuring of diagnosis within multi-level ontologies (as in Derm1M) enables hierarchical MCQA, morphological reasoning, and concept detection aligned with expert workflows. Caption generation pipelines utilize ASR, OCR, LLM-based correction, and manual review to ensure standardized descriptions with context granularity sufficient for clinical interpretation and fairness evaluation.

2. Neural Architectures and Reasoning Pipelines

Cutting-edge PC-DVLM systems employ hybrid transformer backbones built to support multimodal input fusion and interpretable output. DermLIP models (Yan et al., 19 Mar 2025) utilize dual CLIP-style encoders (ViT–B16 and GPT2/BioMedBERT), contrastively trained on ontology-aligned descriptions. SkinVL (Zeng et al., 9 May 2025) fuses CLIP ViT–L/14 visual features and LLaVA-Med (Mistral–7B), leveraging LoRA adapters for cross-attention and prompt-based instruction tuning.

Modeling advances include:

• Specialist–Generalist frameworks: CLARIFY (Saha et al., 25 Aug 2025) combines a high-precision DINOv2-based image classifier (“Specialist”) with a pruned, LoRA-tuned VLM (“Generalist”), guided by diagnosis embeddings and augmented through gated attention and knowledge graph retrieval.
• Concept Bottlenecks: VL-MedGuide (Yu et al., 8 Aug 2025), CBM/GPT+CBM approaches (Patrício et al., 2023), and DermoGPT’s morphology JSON bottleneck (Ru et al., 5 Jan 2026) enforce explicit detection of granular visual features or concepts (e.g., asymmetry, color, border irregularity) prior to chain-of-thought disease reasoning.
• Chain-of-Thought Supervision: SkinGPT-R1 (Shen et al., 19 Nov 2025) adapts a reasoning-centered VLM with adapter-only dual distillation, leveraging DermCoT narratives aligned to six clinical dimensions (accuracy, safety, coverage, coherence, precision, groundedness).
• Diffusion Transformers: DermDiT (Munia et al., 2 Apr 2025) and SkinGEN (Lin et al., 2024) apply patient-attribute–aware text prompts (via VLM) to condition diffusion models for synthetic dermoscopic image generation, mitigating representation bias for under-sampled subgroups.

Table 1 summarizes select PC-DVLM neural pipeline features.

System	Backbone	Bottleneck/Reasoning	Fairness Modeling
DermLIP	ViT + GPT2	Ontology concepts	Metadata/skin tone
SkinVL	ViT-L + LLaVA	Cross-attention MM	Demographic tags
CLARIFY	DINOv2 + VLM	Diagnosis embeddings	Specialist guiding
VL-MedGuide	LVLM	Concept + CoT	Explicit prompts
DermoGPT	Qwen3-VL	Morph JSON + GRPO-RL	CCT + fairness-RL
SkinGPT-R1	Vision-R1	Adapter CoT	DermEval scoring

3. Conceptual Bottlenecks, Prompt Engineering, and Chain-of-Thought Methodologies

Concept-level interpretation is fundamental to PC-DVLM. Systems such as VL-MedGuide (Yu et al., 8 Aug 2025) and “concept-based embedding models” (Patrício et al., 2023) use carefully engineered prompts (“Describe the color variations observed”; “Is there asymmetry present?”) fed to LVLMs to extract binary (present/absent) and descriptive visual concepts. These outputs form a bottleneck—either as a vector, JSON document, or table—which serves as input for chain-of-thought decoder reasoning.

Chain-of-thought modules translate concept bottleneck outputs to stepwise diagnostic completion, justified with clinical rationale (“Step 1: No asymmetry. Step 2: No color variation. Step 3: Regular borders. Diagnosis: melanocytic nevus.”). Supervision is implemented through multi-task cross-entropy (for concept and disease prediction), human-verified narrative templates (VL-MedGuide), or dedicated CoT corpora (DermCoT in SkinGPT-R1) (Shen et al., 19 Nov 2025). These methodologies support the production of explanations tailored for patient understanding, with features explicitly mapped to risk profiles.

4. Equity, Fairness Metrics, and Synthetic Balancing

PC-DVLM mandates skin-tone and demographic equity as a technical criterion. SkinGPT-4 (Nijjer et al., 28 Sep 2025) demonstrated that accuracy, informativity, physician utility, and patient utility each display statistically significant drops (dp = 0.10–0.15) from Fitzpatrick I/II to V/VI, unless sample reweighting, bias-aware losses, or post-hoc prediction adjustment are employed. DermDiT (Munia et al., 2 Apr 2025) addresses subgroup diagnosis bias by generating synthetic dermoscopic images for underrepresented tone/disease groups (FST I–II/A, III–IV/B, V–VI/C), increasing recall for dark and mid-tone subgroups by an order of magnitude.

Explicit fairness metrics employed include:

• Demographic Parity: $DP = |P(\hat Y=1\mid A=a) - P(\hat Y=1\mid A=b)|$
• Equalized Odds: $EO = \max(|TPR_a-TPR_b|,\, |FPR_a-FPR_b|)$
• Fairness reward component in MAVIC RL: $$R_\mathrm{fair} = -\lambda_\mathrm{fair} \max_{g,g'}|\mathrm{Acc}_g-\mathrm{Acc}_{g'}|$$ (Ru et al., 5 Jan 2026)

Systems like DermoGPT use these metrics in both evaluation (DermoBench fairness axis, minimum/max accuracy across Fitzpatrick groups) and training objectives (MAVIC’s combined reward for taxonomy alignment and fairness).

5. Interactive Question Answering, Segmentation, and Explanations

Patient-centered systems incorporate multi-turn VQA, closed-ended QA, and segmentation to mirror teledermatology workflows and enhance usability. DermaVQA-DAS (Yim et al., 30 Dec 2025) formalizes 36 high-level and 27 fine-grained assessment questions (e.g., “Anatomic location of lesion,” “Border abruptness”) in a schema supporting structured evaluation for both multimodal models and annotators.

Closed-ended QA achieves 0.798 accuracy (o3), while segmentation (using models like BiomedParse, MedSAM) with context-augmented prompts achieves Jaccard index 0.395 and Dice score 0.566. Patient queries are infused directly into model prompts ("Highlight the abnormal skin lesion…"), supporting cross-lingual (English, Chinese) benchmarks. Explanations are evaluated for clarity, correctness, and empathy using Likert-based clinician ratings and LLM-Judge scores.

Interactive VQA pipelines as in SkinVL (Zeng et al., 9 May 2025), CLARIFY (Saha et al., 25 Aug 2025), and Dermacen Analytica (Panagoulias et al., 2024) enable scenario-adaptive dialogue, using diagnosis-guided knowledge retrieval (“Explain what this diagnosis means, common symptoms, and treatments in simple terms”) and clickable evidence trails.

6. Performance Benchmarks and Clinical Deployment

Numerous systems report state-of-the-art performance in domain-specific benchmarks:

• CLARIFY: Diagnostic accuracy 82.1% vs 64.1% for the strongest baseline; VRAM reduction ≥20%; clinician-rated explanation clarity (Saha et al., 25 Aug 2025).
• DermLIP: Outperforms BiomedCLIP, MONET, and generalist CLIP models by 12–43 percentage points in top-1 accuracy and cross-modal retrieval across clinical, dermoscopic, and rare-disease test sets (Yan et al., 19 Mar 2025).
• VL-MedGuide: 83.55% balanced accuracy and 80.12% F1 (diagnosis); 76.10% BACC and 67.45% F1 (concept detection) (Yu et al., 8 Aug 2025).
• DermETAS-SNA: F1-score 56.30% (vs 48.51% for SkinGPT-4); domain-expert agreement >92% (Oruganty et al., 9 Dec 2025).
• SkinGPT-R1: DermBench average score 4.031/5 over six clinical dimensions; 41% improvement above Vision-R1 baseline (Shen et al., 19 Nov 2025).
• DermoGPT: DermoBench fairness score 93.88, approaching human 94.00; RL+CCT narrowing the Human–AI gap in reasoning, diagnosis, and equity (Ru et al., 5 Jan 2026).

Deployment considerations include model size for CPU-only inference (Specialist <90M parameters), pruned LLMs fitting on commodity GPU hardware, local privacy-preserving deployment (SkinGPT-4, CLARIFY), and interactive audit trails referencing knowledge graph nodes.

7. Future Directions: Personalization, Transparency, and Ethics

A plausible implication is that patient-centered advances will depend on systems that fuse input metadata, exposure to rare diagnoses, and human-in-the-loop workflows. Recommendations include augmentation of training sets with systematically curated data across skin tones (Nijjer et al., 28 Sep 2025, Munia et al., 2 Apr 2025), prompt-level personalization (e.g., patient risk factors JSON blocks), extension of concept bottleneck to include risk and preference features (Ru et al., 5 Jan 2026), and on-device light adaptation for robustness (CCT and LoRA gradient steps).

Transparency-driven reward and output auditing (DermoGPT), explicit chain-of-thought narratives (SkinGPT-R1), and lay translation adapters for patient communication are crucial for real-world adoption and trust. Ethical priorities continue to be bias mitigation, avoidance of overreliance on synthetic data, privacy, and clinician supervision, especially in high-stakes or rare-disease scenarios.

Collectively, patient-centered dermatological vision-language modeling is converging on an architecture and workflow paradigm that marries expert-level accuracy, granular interpretability, demographic equity, and actionable patient communication. This positions PC-DVLM as an essential domain within medical multimodal AI, underpinning both clinical decision support and scalable teledermatology.