Skin-R1: Toward Trustworthy Clinical Reasoning for Dermatological Diagnosis

• URL: http://arxiv.org/abs/2511.14900v1
• Date: Tue, 18 Nov 2025 20:38:36 GMT
• Title: Skin-R1: Toward Trustworthy Clinical Reasoning for Dermatological Diagnosis
• Authors: Zehao Liu, Wejieying Ren, Jipeng Zhang, Tianxiang Zhao, Jingxi Zhu, Xiaoting Li, Vasant G. Honavar,
• Abstract summary: SkinR1 is a novel dermatological vision-language model (VLM) that combines deep, textbook-based reasoning with the broad generalization capabilities of reinforcement learning (RL)<n>SkinR1 systematically resolves the key challenges through a unified, end-to-end framework. First, we design a textbook-based reasoning generator that synthesizes high-fidelity, hierarchy-aware, and differential-diagnosis (DDx)-informed trajectories.<n>Second, we leverage the constructed trajectories for supervised fine-tuning (SFT) empowering the model with grounded reasoning ability. Third, we develop a novel RL paradigm that, by incorporating the
• Score: 27.666376727163073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of vision-language models (VLMs) has opened new possibilities for clinical reasoning and has shown promising performance in dermatological diagnosis. However, their trustworthiness and clinical utility are often limited by three major factors: (1) Data heterogeneity, where diverse datasets lack consistent diagnostic labels and clinical concept annotations; (2) Absence of grounded diagnostic rationales, leading to a scarcity of reliable reasoning supervision; and (3) Limited scalability and generalization, as models trained on small, densely annotated datasets struggle to transfer nuanced reasoning to large, sparsely-annotated ones. To address these limitations, we propose SkinR1, a novel dermatological VLM that combines deep, textbook-based reasoning with the broad generalization capabilities of reinforcement learning (RL). SkinR1 systematically resolves the key challenges through a unified, end-to-end framework. First, we design a textbook-based reasoning generator that synthesizes high-fidelity, hierarchy-aware, and differential-diagnosis (DDx)-informed trajectories, providing reliable expert-level supervision. Second, we leverage the constructed trajectories for supervised fine-tuning (SFT) empowering the model with grounded reasoning ability. Third, we develop a novel RL paradigm that, by incorporating the hierarchical structure of diseases, effectively transfers these grounded reasoning patterns to large-scale, sparse data. Extensive experiments on multiple dermatology datasets demonstrate that SkinR1 achieves superior diagnostic accuracy. The ablation study demonstrates the importance of the reasoning foundation instilled by SFT.

Related papers

• PathReasoner-R1: Instilling Structured Reasoning into Pathology Vision-Language Model via Knowledge-Guided Policy Optimization [6.821738567680833]
  We construct PathReasoner, the first large-scale dataset of whole-slide image (WSI) reasoning.<n>PathReasoner-R1 synergizes supervised fine-tuning with reasoning-oriented reinforcement learning to instill structured chain-of-thought capabilities.<n>Experiments demonstrate that PathReasoner-R1 achieves state-of-the-art performance on both PathReasoner and public benchmarks across various image scales.
  arXiv  Detail & Related papers  (2026-01-29T12:21:16Z)
• DermoGPT: Open Weights and Open Data for Morphology-Grounded Dermatological Reasoning MLLMs [54.8829900010621]
  Multimodal Large Language Models (MLLMs) show promise for medical applications, yet progress in dermatology lags due to limited training data, narrow task coverage, and lack of clinically-grounded supervision.<n>We present a comprehensive framework to address these gaps.<n>First, we introduce DermoInstruct, a large-scale morphology-anchored instruction corpus comprising 211,243 images and 772,675 trajectories across five task formats.<n>Second, we establish DermoBench, a rigorous benchmark evaluating 11 tasks across four clinical axes: Morphology, Diagnosis, Reasoning, and Fairness, including a challenging subset of 3,600
  arXiv  Detail & Related papers  (2026-01-05T07:55:36Z)
• Anatomy-R1: Enhancing Anatomy Reasoning in Multimodal Large Language Models via Anatomical Similarity Curriculum and Group Diversity Augmentation [52.7583577508452]
  Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning.<n>Their potential in medical imaging remains underexplored, especially in clinical anatomical surgical images.<n>These challenges limit the effectiveness of conventionalSupervised Fine-Tuning strategies.
  arXiv  Detail & Related papers  (2025-12-22T16:06:36Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction [2.904892426557913]
  Large language models (LLMs) have shown strong performance in biomedical NLP.<n>We present a unified, multi-task learning framework that aligns autoregressive LLMs with clinical reasoning for outcome prediction.<n>Our findings underscore the importance of reasoning-aware alignment in multi-task clinical modeling.
  arXiv  Detail & Related papers  (2025-10-20T13:35:12Z)
• Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks [56.75602443936853]
  One in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder.<n>While prior works use graph neural network (GNN) approaches for disorder prediction, they remain black-boxes, limiting their reliability and clinical translation.<n>In this work, we propose a concept-based diagnosis framework that that encodes interpretable functional connectivity concepts.<n>Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance.
  arXiv  Detail & Related papers  (2025-10-02T19:38:46Z)
• RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis [56.373297358647655]
  Retrieval-Augmented Diagnosis (RAD) is a novel framework that injects external knowledge into multimodal models directly on downstream tasks.<n>RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guideline-enhanced contrastive loss transformer, and a dual decoder.
  arXiv  Detail & Related papers  (2025-09-24T10:36:14Z)
• Self-Supervised Cross-Encoder for Neurodegenerative Disease Diagnosis [6.226851122403944]
  We propose a novel self-supervised cross-encoder framework that leverages the temporal continuity in longitudinal MRI scans for supervision.<n>This framework disentangles learned representations into two components: a static representation, constrained by contrastive learning, which captures stable anatomical features; and a dynamic representation, guided by input-gradient regularization, which reflects temporal changes.<n> Experimental results on the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate that our method achieves superior classification accuracy and improved interpretability.
  arXiv  Detail & Related papers  (2025-09-09T11:52:24Z)
• DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model [92.66916452260553]
  DermNIO is a versatile foundation model for dermatology.<n>It incorporates a novel hybrid pretraining framework that augments the self-supervised learning paradigm.<n>It consistently outperforms state-of-the-art models across a wide range of tasks.
  arXiv  Detail & Related papers  (2025-08-17T00:41:39Z)
• Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models [2.3802351706765017]
  Large, publicly available clinical datasets have emerged as a novel resource for understanding disease.<n>These datasets are derived from data not originally collected for research purposes and, as a result, are often incomplete and lack critical labels.<n>Many AI tools have been developed to retrospectively label these datasets, such as by performing disease classification.<n>Previous work has attempted to explain predictions using Concept Bottleneck Models (CBMs), which learn interpretable concepts that map to higher-level clinical ideas.<n>We use the identification of Acute Respiratory Distress Syndrome (ARDS) as a challenging test case to demonstrate the value of incorporating contextual information from clinical notes to improve
  arXiv  Detail & Related papers  (2025-08-13T11:19:30Z)
• An Explainable Diagnostic Framework for Neurodegenerative Dementias via Reinforcement-Optimized LLM Reasoning [1.5646349560044959]
  We propose a framework that integrates two core components to enhance diagnostic transparency.<n>First, we introduce a modular pipeline for converting 3D T1-weighted brain MRIs into textual radiology reports.<n>Second, we explore the potential of modern Large Language Models (LLMs) to assist clinicians in the differential diagnosis.
  arXiv  Detail & Related papers  (2025-05-26T13:18:32Z)
• SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models [54.32264601568605]
  SkinGEN is a diagnosis-to-generation framework that generates reference demonstrations from diagnosis results provided by VLM.<n>We conduct a user study with 32 participants evaluating both the system performance and explainability.<n>Results demonstrate that SkinGEN significantly improves users' comprehension of VLM predictions and fosters increased trust in the diagnostic process.
  arXiv  Detail & Related papers  (2024-04-23T05:36:33Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.