The paper introduces SMILE, a self‑explainable multimodal information bottleneck framework for medical diagnosis. It jointly optimizes predictive accuracy and modality‑specific explainability by selecting the most informative elements within each data modality. Experiments on diverse medical datasets show strong diagnostic performance, including a 9.1‑percentage‑point accuracy gain on the iCTCF dataset, and provide transparent, modality‑aware explanations that enhance both explainability and generalization.
By Yuqing Yang, Alexander Schmatz, Zhaozhao Ma, Changkyu Choi, Robert Jenssen, Shujian Yu
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
By Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang
The paper introduces a flexible symbolic framework that efficiently computes logical explanations for deep neural networks by parameterizing explanations with internal neuron activations and leveraging general-purpose logical engines like SMT solvers. Unlike previous methods that rely on specialized verifiers or are limited to individual input features, this approach is not restricted in shape and can scale to deep architectures. Experiments on image recognition and medical benchmarks demonstrate improved computational efficiency and the ability to explain networks that were previously intractable for logic-based methods.
By Tom\'a\v{s} Kol\'arik, Faezeh Labbaf, Fabrizio Leopardi, Grigory Fedyukovich, Michael Wand, Natasha Sharygina
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta
arXiv:2603. 07131v4 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis.
By Shuai Lu, Meng Wang, Jia Guo, Jiawei Du, Bo Liu, Shengzhu Yang, Weihang Zhang, Huazhu Fu, Huiqi Li
arXiv:2605. 22547v3 Announce Type: replace-cross Abstract: Medical image diagnosis has achieved significant progress with deep learning, yet existing methods often rely on isolated visual evidence and lack the ability to effectively leverage similar cases and external knowledge.
By Yiming Xu, Yixuan Liu, Yuhang Zhang, Ling Zheng, Yihan Wang, Qi Song
arXiv:2512.13742v3 Announce Type: replace-cross
Abstract: Medical image classifiers detect gastrointestinal diseases well, but they do not explain their decisions. Large language models can generate...
By Md. Najib Hasan (Wichita State University, USA), Imran Ahmad (Wichita State University, USA), Sourav Basak Shuvo (Khulna University of Engineering and Technology, Bangladesh), Md. Mahadi Hasan Ankon (Khulna University of Engineering and Technology, Bangladesh), Nazmul Siddique (Ulster University, UK), Hui Wang (Queen's University Belfast, UK)
arXiv:2508. 16129v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm.
By Ruiqi Wu, Yuang Yao, Tengfei Ma, Chenran Zhang, Na Su, Tao Zhou, Geng Chen, Wen Fan, Yi Zhou
arXiv:2607. 25933v1 Announce Type: cross Abstract: Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning.
By Rui Yang, Weihao Xuan, Yi Lin, Zhuhan Bao, Jonathan Chong Kai Liew, Matthew Yu Heng Wong, Nicol\'as Lescano, Nikita R. Paripati, Emily Ling-Lin Pai, Jiarui Liu, Heli Qi, Heng-Jui Chang, Benny Kai Guo Loo, Huitao Li, Kunyu Yu, Yufan Wang, Chuan Hong, Shijian Lu, Douglas Teodoro, Naoto Yokoya, Ross Koppel, Mona Diab, Hua Xu, David W. Bates, Nan Liu, Yifan Peng
arXiv:2610.08528v1 Announce Type: cross
Abstract: Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image featu...
By Asim Khan, Samee Ullah Khan, Dwarikanath Mahapatra
The paper introduces a neurosymbolic verification framework that audits the internal consistency of vision-language models (VLMs) when generating radiology reports. By autoformalizing free‑text findings into structured propositional evidence and using an SMT solver (Z3) alongside a clinical knowledge base, the system determines whether each diagnostic claim is entailed, hallucinated, or omitted. Evaluation on seven VLMs across five chest X‑ray benchmarks reveals distinct reasoning failure modes—such as conservative observation and stochastic hallucination—that traditional metrics miss, and shows that enforcing solver‑backed entailment can eliminate unsupported hallucinations and improve diagnostic soundness and precision.
By Vikash Singh, Debargha Ganguly, Haotian Yu, Chengwei Zhou, Prerna Singh, Brandon Lee, Vipin Chaudhary, Gourav Datta
arXiv:2504. 06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited.
By Lisa Herzog, Jonas Br\"andli, Maurice Schneeberger, Loran Avci, Nordin Dari, Martin H\"ansel, Hakim Baazaoui, Pascal B\"uhler, Susanne Wegener, Beate Sick