Medical Diagnosis

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Overview

Medical diagnosis studies how to combine symptoms, records, laboratory tests, medical images, physiological signals, and molecular data to identify disease and support clinical decisions. Methods range from conventional machine learning and deep models such as CNNs and graph networks to multimodal foundation models, retrieval-augmented language models, and agents that ask questions or request tests as evidence accumulates. Systems are evaluated on tasks including image and signal classification, electronic-record prediction, and conversational diagnosis, using resources such as MIMIC and ADNI alongside disease-specific and external-site cohorts. A central challenge is that strong benchmark results may not transfer to real clinics, where data are incomplete or shifted and models must provide calibrated, evidence-grounded recommendations without worsening bias or unsafe decisions.

Latest papers

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263 papers26 papers in the last four weeks, down 13% on the four weeks before. 0.2% of all new papers.

Feb 26, 2026

Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots

Human-in-the-loop validation is essential in safety-critical clinical AI, yet the transition between initial model inference and expert correction is rarely analyzed as a structured signal. We introduce a diagnostic alignment framework in which the AI-generated image based report is preserved as an immutable inference state and systematically compared with the physician-validated outcome. The inference pipeline integrates a vision-enabled large language model, BERT- based medical entity extraction, and a Sequential Language Model Inference (SLMI) step to enforce domain-consistent refinement prior to expert review. Evaluation on 21 dermatological cases (21 complete AI physician pairs) em- ployed a four-level concordance framework comprising exact primary match rate (PMR), semantic similarity-adjusted rate (AMR), cross-category alignment, and Comprehensive Concordance Rate (CCR). Exact agreement reached 71.4% and remained unchanged under semantic similarity (t = 0.60), while structured cross-category and differential overlap analysis yielded 100% comprehensive concordance (95% CI: [83.9%, 100%]). No cases demonstrated complete diagnostic divergence. These findings show that binary lexical evaluation substantially un- derestimates clinically meaningful alignment. Modeling expert validation as a structured transformation enables signal-aware quantification of correction dynamics and supports traceable, human aligned evaluation of image based clinical decision support systems.

Dimitrios P. Panagoulias, Evangelia-Aikaterini Tsichrintzi, Georgios Savvidis +1

Department of Informatics, University of Piraeus, Karaoli ke Dimitriou 80, Piraeus, 18534, Greece · Department of Research & Development, Noetiv PC, Valaoritou 18, Athens, 10671, Greece · Department of Dermatology, Dermacen SA, Valaoritou 18, Athens, 10671, Greece

DermatologyClinical Decision SupportMedical DiagnosisMedical VLMs
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Feb 25, 2026

MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to 7 distinct visual clinical evidence (CE) types per case. Mirroring clinical workflow, we evaluate 18 MLLMs on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. While top models often match or even outperform human experts on DDx generation, all MLLMs exhibit a much larger DDx--FDx performance gap compared to expert clinicians, indicating a failure mode in synthesis of heterogeneous CE types. Ablations attribute this failure to (i) overreliance on less discriminative textual CE (e.g.\it{e.g.}, medical history) and (ii) a cross-modal CE utilization gap. We introduce Evidence Sensitivity to quantify the latter and show that a smaller gap correlates with higher diagnostic accuracy. Finally, we demonstrate how it can be used to guide interventions to improve model performance. We will open-source our benchmark and code.

Boqi Chen, Xudong Liu, Jiachuan Peng +5

ETH Zurich · MBZUAI · Amazon +4

Medical DiagnosisMedical VLMsMultimodal Model EvaluationMultimodal Reasoning
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Feb 23, 2026

Forgetting-Resistant and Lesion-Aware Source-Free Domain Adaptive Fundus Image Analysis with Vision-Language Model

Source-free domain adaptation (SFDA) aims to adapt a model trained in the source domain to perform well in the target domain, with only unlabeled target domain data and the source model. Taking into account that conventional SFDA methods are inevitably error-prone under domain shift, recently greater attention has been directed to SFDA assisted with off-the-shelf foundation models, e.g., vision-language (ViL) models. However, existing works of leveraging ViL models for SFDA confront two issues: (i) Although mutual information is exploited to consider the joint distribution between the predictions of ViL model and the target model, we argue that the forgetting of some superior predictions of the target model still occurs, as indicated by the decline of the accuracies of certain classes during adaptation; (ii) Prior research disregards the rich, fine-grained knowledge embedded in the ViL model, which offers detailed grounding for fundus image diagnosis. In this paper, we introduce a novel forgetting-resistant and lesion-aware (FRLA) method for SFDA of fundus image diagnosis with ViL model. Specifically, a forgetting-resistant adaptation module explicitly preserves the confident predictions of the target model, and a lesion-aware adaptation module yields patch-wise predictions from ViL model and employs them to help the target model be aware of the lesion areas and leverage the ViL model's fine-grained knowledge. Extensive experiments show that our method not only significantly outperforms the vision-language model, but also achieves consistent improvements over the state-of-the-art methods. Our code will be released.

Zheang Huai, Hui Tang, Hualiang Wang +1

The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Source-Free Domain AdaptationVLM AdaptationFundus ImagingMedical Diagnosis
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Feb 20, 2026

RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis

Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of tissue samples after chemical staining, a time-consuming process requiring expert analysis. Raman spectroscopy is an alternative, stain-free method of extracting information from samples. Using nnU-Net, we trained a segmentation model on a novel dataset of spatial Raman spectra aligned with tumour annotations, achieving a mean foreground Dice score of 80.9%, surpassing previous work. Furthermore, we propose a novel, interpretable, prototype-based architecture called RamanSeg. RamanSeg classifies pixels based on discovered regions of the training set, generating a segmentation mask. Two variants of RamanSeg allow a trade-off between interpretability and performance: one with prototype projection and another projection-free version. The projection-free RamanSeg outperformed a U-Net baseline with a mean foreground Dice score of 67.3%, offering a meaningful improvement over a black-box training approach.

Chris Tomy, Mo Vali, David Pertzborn +9

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK · Department of Physics, University of Cambridge, Cambridge, UK · Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany +2

Image SegmentationMedical DiagnosisExplainable Medical Image AnalysisPrototype LearningRaman Spectroscopy
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Feb 19, 2026

Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection

Skin cancer can be life-threatening if not diagnosed early, a prevalent yet preventable disease. Globally, skin cancer is perceived among the finest prevailing cancers and millions of people are diagnosed each year. For the allotment of benign and malignant skin spots, an area of critical importance in dermatological diagnostics, the application of two prominent deep learning models, VGG16 and DenseNet201 are investigated by this paper. We evaluate these CNN architectures for their efficacy in differentiating benign from malignant skin lesions leveraging enhancements in deep learning enforced to skin cancer spotting. Our objective is to assess model accuracy and computational efficiency, offering insights into how these models could assist in early detection, diagnosis, and streamlined workflows in dermatology. We used two deep learning methods DenseNet201 and VGG16 model on a binary class dataset containing 3297 images. The best result with an accuracy of 93.79% achieved by DenseNet201. All images were resized to 224x224 by rescaling. Although both models provide excellent accuracy, there is still some room for improvement. In future using new datasets, we tend to improve our work by achieving great accuracy.

Mohammad Tahmid Noor, B. M. Shahria Alam, Tasmiah Rahman Orpa +3

Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh

DermatologySkin Lesion ClassificationMedical DiagnosisMedical Image ClassificationConvolutional Neural Networks
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Feb 19, 2026

A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

The significant advancements in computational power cre- ate a vast opportunity for using Artificial Intelligence in different ap- plications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medi- cal specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available tech- nology offered by Tensorflow and Keras along with Microsoft-developed Vision Transformer, that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hy- brid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information.

Asif Hasan Chowdhury, Md. Fahim Islam, M Ragib Anjum Riad +3

BRAC University, Dhaka-1212, Bangladesh

Medical DiagnosisChest X-Ray ClassificationHybrid CNN-Transformer ArchitecturesEnsemble LearningFederated Learning
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Feb 19, 2026

Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis

Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analysis is promising, atlas-based representations may obscure individualized anatomy. Gyral folding-based networks using three-hinge gyri provide a biologically grounded alternative, but inter-individual variability in cortical folding results in inconsistent landmark correspondence and highly irregular network sizes, violating the fixed-topology and node-alignment assumptions of most existing graph learning methods, particularly in clinical datasets where pathological changes further amplify anatomical heterogeneity. We therefore propose a probability-invariant random-walk-based framework that classifies individualized gyral folding networks without explicit node alignment. Cortical similarity networks are built from local morphometric features and represented by distributions of anonymized random walks, with an anatomy-aware encoding that preserves permutation invariance. Experiments on a large clinical cohort of AD and LBD subjects show consistent improvements over existing gyral folding and atlas-based models, demonstrating robustness and potential for dementia diagnosis.

Minheng Chen, Tong Chen, Chao Cao +4

Department of Computer Science and Engineering, University of Texas at Arlington, United States · School of Computing, University of Georgia, United States · Department of Psychiatry, University of Cambridge, United Kingdom +1

Alzheimer's Disease ClassificationBrain Network AnalysisMedical DiagnosisGraph Representation Learning
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Feb 19, 2026

Position: Evaluation of ECG Representations Must Be Fixed

This position paper argues that current benchmarking practice in 12-lead ECG representation learning must be fixed to ensure progress is reliable and aligned with clinically meaningful objectives. The field has largely converged on three public multi-label benchmarks (PTB-XL, CPSC2018, CSN) dominated by arrhythmia and waveform-morphology labels, even though the ECG is known to encode substantially broader clinical information. We argue that downstream evaluation should expand to include an assessment of structural heart disease and patient-level forecasting, in addition to other evolving ECG-related endpoints, as relevant clinical targets. Next, we outline evaluation best practices for multi-label, imbalanced settings, and show that when they are applied, the literature's current conclusion about which representations perform best is altered. Furthermore, we demonstrate the surprising result that a randomly initialized encoder with linear evaluation matches state-of-the-art pre-training on many tasks. This motivates the use of a random encoder as a reasonable baseline model. We substantiate our observations with an empirical evaluation of five representative ECG pre-training approaches across six evaluation settings: the three standard benchmarks, a structural disease dataset, hemodynamic inference, and patient forecasting.

Zachary Berger, Daniel Prakah-Asante, John Guttag +1

Massachusetts Institute of Technology, Cambridge, MA, USA · Massachusetts General Hospital, Boston, MA, USA

Electrocardiogram Representation LearningRepresentation LearningElectrocardiogram ClassificationElectrocardiogram InterpretationClinical PredictionMedical Diagnosis
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Feb 17, 2026

Optimization Instability in Autonomous Agentic Workflows for Clinical Symptom Detection

Autonomous agentic workflows that iteratively refine their own behavior hold considerable promise, yet their failure modes remain poorly characterized. We investigate optimization instability, a phenomenon in which continued autonomous improvement paradoxically degrades classifier performance, using Pythia, an open-source framework for automated prompt optimization. Evaluating three clinical symptoms with varying prevalence (shortness of breath at 23%, chest pain at 12%, and Long COVID brain fog at 3%), we observed that validation sensitivity oscillated between 1.0 and 0.0 across iterations, with severity inversely proportional to class prevalence. At 3% prevalence, the system achieved 95% accuracy while detecting zero positive cases, a failure mode obscured by standard evaluation metrics. We evaluated two interventions: a guiding agent that actively redirected optimization, amplifying overfitting rather than correcting it, and a selector agent that retrospectively identified the best-performing iteration successfully prevented catastrophic failure. With selector agent oversight, the system outperformed expert-curated lexicons on brain fog detection by 331% (F1) and chest pain by 7%, despite requiring only a single natural language term as input. These findings characterize a critical failure mode of autonomous AI systems and demonstrate that retrospective selection outperforms active intervention for stabilization in low-prevalence classification tasks.

Cameron Cagan, Pedram Fard, Jiazi Tian +3

Department of Medicine, Massachusetts General Hospital, Boston, MA, USA · Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

LLM Agent Workflow OptimizationAI Agent ReliabilityAutomatic Prompt OptimizationMedical Diagnosis
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Feb 16, 2026

Image-based Joint-level Detection for Inflammation in Rheumatoid Arthritis from Small and Imbalanced Data

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic joint inflammation. Early diagnosis and tight follow-up are essential to the management of RA, as ongoing inflammation can cause irreversible joint damage. The detection of arthritis is important for diagnosis and assessment of disease activity; however, it often takes a long time for patients to receive appropriate specialist care. Therefore, there is a strong need to develop systems that can detect joint inflammation easily using RGB images captured at home. Consequently, we tackle the task of RA inflammation detection from RGB hand images. This task is highly challenging due to general issues in medical imaging, such as the scarcity of positive samples, data imbalance, and the inherent difficulty of the task itself. However, to the best of our knowledge, no existing work has explicitly addressed these challenges in RGB-based RA inflammation detection. This paper quantitatively demonstrates the difficulty of visually detecting inflammation by constructing a dedicated dataset, and we propose a inflammation detection framework with global local encoder that combines self-supervised pretraining on large-scale healthy hand images with imbalance-aware training to detect RA-related joint inflammation from RGB hand images. Our experiments demonstrated that the proposed approach improves F1-score by 0.2 points and Gmean by 0.25 points compared with the baseline model.

Shun Kato, Yasushi Kondo, Shuntaro Saito +2

Keio University, Japan · Keio University

Medical DiagnosisMedical Image Classification
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Feb 11, 2026

A swap-adversarial framework for domain generalization in pre- and post-stimulation Parkinsonian state classification

We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data. It achieves robust domain generalization across ECoG and electroencephalography (EEG)-based brain-computer interface datasets. Our framework integrates (1) robust preprocessing, (2) inter-subject balanced channel swap (ISBCS) for cross-subject augmentation, and (3) domain-adversarial learning (DAL) to suppress subject-specific bias. The ISBCS method is a bio-inspired channel swapping strategy that exchanges only functionally corresponding channels across subjects, guided by a brain map, to mitigate inter-subject distribution differences. The DAL strategy encourages the model to learn task-relevant shared features. In addition, we introduce a new ECoG dataset, the first reproducible benchmark, which is constructed from long-term ECoG recordings of 6-hydroxydopamine-induced rat models and annotated with neural responses measured before and after electrical stimulation. We validate the effectiveness of this framework through extensive experiments under cross-subject, cross-session, and cross-dataset settings. Our results show that the proposed framework exhibits the best and robust generalization performance both for our newly constructed ECoG and public EEG datasets.

Seongwon Jin, Hanseul Choi, Sunggu Yang +2

Department of Computer Science and Engineering, Incheon National University, Yeonsu-gu, 22012, Incheon, Republic of Korea · Department of Nanobioengineering, Incheon National University, Yeonsu-gu, 22012, Incheon, Republic of Korea · Center for Brain-Machine Interface, Incheon National University, Yeonsu-gu, 22012, Incheon, Republic of Korea +1

Adversarial TrainingData AugmentationMedical DiagnosisCross-Domain GeneralizationParkinson's Disease Classification
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Feb 2, 2026

Think Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs

Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomplete information. Existing approaches often rely on the parametric knowledge of a model or assume that patients provide rich and concrete information, which is unrealistic. To address these limitations, we propose a conversational diagnosis system that explores a diagnostic knowledge graph to reason in two steps: (i) generating diagnostic hypotheses from the dialogue context, and (ii) verifying hypotheses through clarifying questions, which are repeated until a final diagnosis is reached. Since evaluating the system requires a realistic patient simulator that responds to the system's questions, we adopt PatientSim, a persona-driven patient simulator, together with patient profiles from MIMIC-IV. We further adapt it with low-specificity symptom reporting to reflect how real-world patients describe symptoms vaguely during early clinical encounters. Experiments show improved diagnostic accuracy and efficiency over strong baselines, and physician evaluations support the realism of our simulator and the clinical utility of the generated clarifying questions. Our code will be released upon publication.

Jeongmoon Won, Seungwon Kook, Yohan Jo

Graduate School of Data Science, Seoul National University

HealthcareMedical DiagnosisClinical ReasoningDifferential Diagnosis
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Jan 28, 2026

ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction

Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high acquisition costs and reliance on expert annotations, limiting the availability of large-scale labeled datasets. In contrast, electrocardiograms (ECGs) are inexpensive, widely accessible, and offer a promising modality for conditioning the generative synthesis of cine CMR. To this end, we propose ECGFlowCMR, a novel ECG-to-CMR generative framework that integrates a Phase-Aware Masked Autoencoder (PA-MAE) and an Anatomy-Motion Disentangled Flow (AMDF) to address two fundamental challenges: (1) the cross-modal temporal mismatch between multi-beat ECG recordings and single-cycle CMR sequences, and (2) the anatomical observability gap due to the limited structural information inherent in ECGs. Extensive experiments on the UK Biobank and a proprietary clinical dataset demonstrate that ECGFlowCMR can generate realistic cine CMR sequences from ECG inputs, enabling scalable pretraining and improving performance on downstream cardiac disease classification and phenotype prediction tasks.

Xiaocheng Fang, Zhengyao Ding, Guangkun Nie +9

State Key Laboratory of General Artificial Intelligence, Peking University · School of Intelligence Science and Technology, Peking University, Beijing, China · Polytechnic Institute of Zhejiang University, Zhejiang University, Hangzhou, China +4

Synthetic Data PretrainingMedical DiagnosisClinical PredictionMedical Image SynthesisConditional Generative ModelingCardiac MRI
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Dec 26, 2025

Region-Specific Calibration Achieves Excellent Inter-Device Reliability for Smartphone Dermatology: A Multi-Device Benchmark on Korean Facial Skin

Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitative multi-device benchmarks remain scarce. Materials and Methods: We analyzed matched facial images from 965 Korean subjects captured by a digital single-lens reflex (DSLR) camera, a consumer tablet, and a consumer smartphone, and evaluated two calibration methods against the DSLR reference. The methods are standard global linear Color Correction Matrix (CCM) normalization and region-specific CCM trained per anatomical region, both applied in Commission Internationale de l'Eclairage Lab* (CIELAB) space. Results: Linear CCM reduced inter-device color differences by 61-74% and placed both Melanin Index (intraclass correlation coefficient [ICC] = 0.80) and Individual Typology Angle (ITA, ICC = 0.78) in the good reliability band. Region-specific CCM raised both indices into the excellent reliability band (MI ICC = 0.95, ITA ICC = 0.93), with anatomical region exceeding the source device as the largest pre-calibration variance contributor (analysis-of-variance η2=0.18η^2 = 0.18 versus 0.12). Conclusion: Consumer-device skin colorimetry therefore achieves clinically useful inter-device reliability using standard calibration, with region-aware calibration the largest remaining source of improvement.

Sungwoo Kang, Jong-Kook Kim

Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea · School of Electrical Engineering, Korea University, Seoul, Republic of Korea

Medical DiagnosisDermatologyMedical Image Benchmarks
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Dec 19, 2025

Towards Explainable Conversational AI for Early Diagnosis with Large Language Models

Healthcare systems around the world are grappling with issues such as inefficient diagnostics, rising costs, and limited access to specialists. These challenges often contribute to delays in treatment and poorer health outcomes. Most existing AI and deep learning based health assessment systems offer limited interactivity and transparency, reducing their usefulness for user-centered health support. This research introduces a conversational chatbot powered by a Large Language Model (LLM), using GPT-4o, Retrieval-Augmented Generation, and explainable AI techniques. The chatbot engages users in a dynamic conversation to extract and normalize symptoms while identifying and ranking potential health conditions through similarity matching and adaptive questioning. Using Chain-of-Thought prompting, the system also provides more transparent explanations of its reasoning process. When evaluated against traditional machine learning models, including Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN using both TF-IDF and CountVectorizer feature extraction, the proposed LLM-based system achieved a Top-1 accuracy of 90% and a Top-3 accuracy of 100%. The system was additionally evaluated through a cross-sectional expert evaluation involving 17 physicians across all 14 conditions, with the results indicating generally favorable assessments of conversational quality, early diagnostic plausibility, and safety-related criteria. These findings demonstrate the potential of explainable conversational AI as a health and well-being support tool for early symptom assessment. However, the proposed system is not intended for clinical diagnosis or clinical decision-making, and further validation would be required before any use in healthcare practice.

Maliha Tabassum, M Shamim Kaiser

Department of Information and Communication Technology Bangladesh University of Professionals Dhaka 1216, Bangladesh · Institute of Information Technology Jahangirnagar University Dhaka 1342, Bangaldesh

Medical DiagnosisConversational AgentsMedical LLMsExplainable Artificial Intelligence
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Nov 18, 2025

Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models

Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis. However, their trustworthiness and clinical utility remain limited by three key challenges: heterogeneous datasets with inconsistent diagnostic labels and concept annotations, the lack of grounded diagnostic rationales for reliable reasoning supervision, and limited scalability when transferring knowledge from small, densely annotated datasets to large collections with sparse labels. To address these challenges, we propose Skin-R1, a dermatology-oriented VLM that integrates textbook-grounded clinical reasoning supervision with reinforcement learning (RL) to improve the accuracy and robustness of diagnostic prediction. First, we construct a textbook-based reasoning generator that synthesizes hierarchy-aware and differential-diagnosis (DDx) diagnostic trajectories derived from authoritative dermatology knowledge. Second, these trajectories are used for supervised fine-tuning (SFT), establishing a clinically grounded reasoning foundation for the model. Finally, we introduce an RL training framework that incorporates the hierarchical structure of dermatological diseases into the reward design, enabling the model to generalize grounded diagnostic reasoning to large-scale datasets with sparse annotations. Extensive experiments across multiple dermatology benchmarks demonstrate that Skin-R1 consistently improves diagnostic accuracy and robustness compared to state-of-the-art Med-VLM baselines. Ablation studies further highlight the critical role of grounded reasoning supervision introduced during the SFT stage.

Zehao Liu, Weijieying Ren, Jipeng Zhang +4

Pennsylvania State University, University Park, PA 16802, USA · Stanford University, Stanford, CA 94305, USA · Hong Kong University of Science and Technology, Hong Kong, China

VLM RobustnessMedical DiagnosisMedical VLMsVLM ReasoningRL for VLMsDermatology
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Oct 17, 2025

BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models

The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts. This lack of transparency and failure to capture the multi-faceted nature of clinical diagnosis, which relies on integrating diverse observations, limits their trustworthiness in high-stakes settings. To address this, we introduce BiomedXPro, an evolutionary framework that leverages a large language model as both a biomedical knowledge extractor and an adaptive optimizer to automatically generate a diverse ensemble of interpretable, natural-language prompt pairs for disease diagnosis. Experiments on multiple biomedical benchmarks show that BiomedXPro consistently outperforms state-of-the-art prompt-tuning methods, particularly in data-scarce few-shot settings. Furthermore, our analysis demonstrates a strong semantic alignment between the discovered prompts and statistically significant clinical features, grounding the model's performance in verifiable concepts. By producing a diverse ensemble of interpretable prompts, BiomedXPro provides a verifiable basis for model predictions, representing a critical step toward the development of more trustworthy and clinically-aligned AI systems.

Kaushitha Silva, Mansitha Eashwara, Sanduni Ubayasiri +2

University of Peradeniya Peradeniya, Sri Lanka · RMIT University Melbourne, Australia

Medical VLMsExplainable Medical Image AnalysisMedical Diagnosis
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Aug 10, 2025

EndoCogniAgent: Closed-Loop Agentic Reasoning with Self-Consistency Validation for Endoscopic Diagnosis

Endoscopic diagnosis is an iterative process in which clinicians acquire, compare, and verify local visual evidence before reaching a conclusion. Current AI systems do not adequately support this process because fine-grained evidence acquisition and multi-step reasoning remain weakly coupled, complicating reconciliation of image-derived findings with their textual interpretations. This gives rise to two failure modes, hallucinated evidence and uncorrected error accumulation, that undermine diagnostic reliability. We propose EndoCogniAgent, a closed-loop agentic framework that formulates endoscopic diagnosis as a controlled state update process for integrating complementary visual and textual evidence. At each reasoning round, a central planner selects an evidence acquisition action, specialized expert tools extract spatial and semantic observations as structured textual evidence, and a self-consistency validation mechanism examines this evidence along two dimensions, knowledge consistency against the input image and temporal consistency with prior validated findings, before updating the diagnostic state. Validated observations are admitted into the evolving state to condition subsequent planning, while insufficiently supported or conflicting findings are retained with corrective feedback that redirects the planner toward additional verification. We further introduce EndoAgentBench, a workflow-oriented benchmark comprising 6,132 question-answer pairs from 11 endoscopic datasets, to evaluate diagnostic agents across a comprehensive diagnostic chain, from fine-grained visual perception to high-level diagnostic reasoning. EndoCogniAgent achieves 85.23% overall accuracy on perception tasks and 71.13% clinical acceptance rate on reasoning tasks. Blinded clinician evaluation further shows consistent improvements in diagnostic response quality over the evaluated baselines.

Yi Tang, Kai-Ni Wang, Liang-Peng Pu +3

School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China · Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University, Nanjing 211189, China · State Key Laboratory of Digital Medical Engineering, Southeast University, Nanjing 211189, China +2

EndoscopyMedical ImagingMedical DiagnosisAgentic ReasoningClinical ReasoningMedical Image Benchmarks+1
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Jul 4, 2025

Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease

Discrepancies between clinical diagnoses and omics profiles within a characterized cohort may reflect misdiagnosis, hidden subgroups or prodromal disease states. We propose MLASDO, a tool to detect and characterize such discrepancies before downstream analyses. MLASDO (1) detects outliers using two unsupervised methods, thereby flagging potential poor-quality samples; and (2) identifies and characterizes anomalous samples (ASs), i.e., individuals whose molecular profile resembles the opposite clinical class. We applied MLASDO to the Parkinson's Progression Markers Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP) Parkinson's disease (PD) cohorts. In PPMI, it detected 26 outliers and 12 ASs: 5 anomalous healthy controls (AHCs) and 7 anomalous PD cases (APDs). AHCs exhibited higher cerebrospinal fluid (CSF) A\b{eta}1-42 levels than controls (P < 0.0396), suggesting resistance to cognitive decline. AHC-specific genes were enriched for the MAPK pathway (P<0.0145), implicated in PD pathogenesis. One AHC later received a different neurological disorder, three months after enrollment. In PDBP, it identified 10 outliers and 8 ASs: 2 AHCs and 6 APDs. One AHC exhibited 24 clinical features consistent with a PD-like phenotype, including severe motor impairment. Results are compiled in an interactive report for inspection and querying, highlighting clinically meaningful individuals otherwise overlooked in conventional analyses.

José A. Pardo-Pérez, Tomás Bernal, Jaime Ñiguez +5

Department of Information and Communications Engineering, Faculty of Computer Science, University of Murcia, Murcia, Spain. · Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. · Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA. +1

Medical DiagnosisUnsupervised Anomaly DetectionParkinson's DiseaseParkinson's Disease Classification
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May 21, 2025

Benchmarking Classification Tasks of Chest X-ray AI Across Multinational Public and Private Datasets: a retrospective diagnostic accuracy study

Chest X-ray (CXR) AI models are increasingly proposed for triage, screening, and rule-out, where the operating point is fixed to miss few abnormalities. Most evaluations instead report threshold-free discrimination on a few public benchmarks, leaving clinically usable performance and generalisability to unseen institutional cohorts poorly characterised. We retrospectively evaluated 10 published CXR classification models on 12 datasets from six countries: seven public benchmarks and five institutional cohorts from China, Japan, and Germany. Models spanned convolutional networks, vision-language models, a question-answering foundation model, and a repurposed encoder. Only image-level classification was assessed; segmentation, detection, and report generation were out of scope. Source labels were harmonised to a common vocabulary. The primary outcome was specificity at 95% sensitivity; PPV at 95% sensitivity and AUROC were secondary outcomes. Estimates weighted each diagnostic task equally, with 95% CIs. MAVL and MedKLIP had the highest overall specificity at 95% sensitivity (0.37 [95% CI 0.31-0.44] and 0.37 [0.30-0.44]) and kept that ranking on tasks evaluable for every model. Their PPV was 0.18 (0.13-0.24) and 0.19 (0.13-0.25): most flagged examinations would be false positives. Specificity was lower on institutional cohorts than on public benchmarks for most models, by 0.13 for MAVL, 0.15 for DenseNet, and 0.09 for MedKLIP. No model was best for every finding, country, lesion extent, or prevalence stratum; specificity fell from 0.34 for diffuse to 0.15 for focal findings. At 95% sensitivity, the best current models clear only about a third of truly negative examinations, and public-benchmark performance overstates what they achieve on unseen institutional data. Operating-point-aware evaluation and external validation on institutional cohorts should precede deployment.

Qinmei Xu, Yiheng Li, Wenya Zhao +21

Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, USA · Department of Radiology, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China · Stanford Breast Imaging, Department of Radiology, Stanford University, Stanford, CA, USA +3

Chest X-Ray ClassificationMedical Image BenchmarksMedical DiagnosisDomain Generalization
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Feb 3, 2025

A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors

Mental health problems such as stress, anxiety, and depression affect millions of people worldwide. These conditions are usually assessed using questionnaires, which rely on how people describe their own feelings. In this study, we explore whether a wearable device can help measure mental health using physical signals from the body. The device records small changes in blood flow and tissue activity from the fingertip. We collected data from 132 adults across 19 countries and compared these signals with mental health questionnaire results. We found that patterns in blood flow and tissue activity are linked to stress-related symptoms. This approach may help develop new tools for simple, non-invasive mental health monitoring in everyday life. Code and datasets are publicly available: https://github.com/leduckhai/Wearable_LDF-FS

Minh Ngoc Nguyen, Khai Le-Duc, Tan-Hanh Pham +7

Aston University, Birmingham, UK · University of Toronto, Toronto, Canada · University Health Network, Toronto, Canada +5

Medical DiagnosisMental HealthWearable SensingWearable Health Monitoring
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Mar 7, 2024

Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering

In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios, along with clinician-approved reasoned answers. Additionally, we implement a prompt driven by Chain of Thought (CoT) reasoning, CLINICR, to mirror the prospective process of incremental reasoning, reaching a correct response to medical questions. We empirically demonstrate how CLINICR outperforms the state-of-the-art 5-shot CoT-based prompt (Liévin et al., 2022). We also present an approach that mirrors real-life clinical practice by first exploring multiple differential diagnoses through MCQ-CLINICR and subsequently narrowing down to a final diagnosis using MCQ-ELIMINATIVE. Finally, emphasizing the importance of response verification in medical settings, we utilize a reward model mechanism, replacing the elimination process performed by MCQ-ELIMINATIVE.

Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5

2Indian Institute of Technology Bombay · 1IBM Research

HealthcareMedical DiagnosisCoT ReasoningFew-Shot PromptingClinical QAStructured Reasoning+2
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Sep 30, 2023

Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning

Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. However, it remains challenging due to data complexity and limited labeled samples. This research paper presents a novel machine-learning approach for fetal health classification, leveraging a LightGBM classifier trained on a comprehensive dataset. The proposed model achieves an impressive accuracy of 98.31% on a test set. Our findings demonstrate the potential of machine learning in enhancing fetal health classification, offering a more objective and accurate assessment. Notably, our approach combines various features, such as fetal heart rate, uterine contractions, and maternal blood pressure, to provide a comprehensive evaluation. This methodology holds promise for improving early detection and treatment of fetal health issues, ensuring better outcomes for both mothers and babies. Beyond the high accuracy achieved, the novelty of our approach lies in its comprehensive feature selection and assessment methodology. By incorporating multiple data points, our model offers a more holistic and reliable evaluation compared to traditional methods. This research has significant implications in the field of obstetrics, paving the way for advancements in early detection and intervention of fetal health concerns. Future work involves validating the model on a larger dataset and developing a clinical application. Ultimately, we anticipate that our research will revolutionize the assessment and management of fetal health, contributing to improved healthcare outcomes for expectant mothers and their babies.

Sujith K Mandala

Medical DiagnosisClinical Prediction
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