ABSTRACT Artificial Intelligence (AI) is increasingly utilised in medicine; however, its “black‐box” nature continues to hinder clinical trust, adoption, and validation. Explainable AI (XAI) has emerged as a critical field to address these transparency challenges by making AI‐driven decisions more interpretable and actionable. This narrative review examines the progress of XAI in medicine over the past decade. We first introduce fundamental XAI concepts and describe our review methodology, followed by a comprehensive analysis of key application domains, including medical imaging, electronic health records (EHRs), and multi‐omics data. Methodologically, we categorise XAI techniques into model‐agnostic approaches (e.g., SHAP, LIME, Anchors) and model‐specific approaches (e.g., Grad‐CAM, LRP, TreeSHAP). Beyond summarising their principles, advantages, and limitations, we further provide a systematic analysis of clinical reliability and failure modes associated with each class of methods, highlighting how explanation techniques may produce misleading, unstable, or non‐causal interpretations in real‐world clinical settings. The review then discusses the demonstrated benefits of XAI, including result validation, bias detection, and improved patient–clinician communication, while critically examining persistent challenges such as limited clinical deployment, inconsistent evaluation standards, and the lack of prospective validation. Finally, we outline future research directions, emphasising the need to adapt XAI to large‐scale foundation models and conversational AI systems, as well as to extend its applicability in biomedical and multi‐omics interpretation. We argue that while XAI is essential for improving transparency, trust, and clinical adoption, its reliable and scalable integration into clinical workflows requires both methodological innovation and rigorous, clinically grounded validation frameworks.

ABSTRACT Accurate medical image segmentation is essential for disease diagnosis, treatment planning and outcome monitoring. However, current segmentation methods heavily rely on large‐scale, pixel‐level annotations, which are costly and labour‐intensive to obtain. To address this challenge, we propose TriS‐Net (Triple‐Supervision Segmentation Network) , a progressive framework that integrates image‐level, bounding box‐level and pixel‐level labels into a multigranularity supervision pipeline under limited annotation settings. In the first stage, TriS‐Net uses image‐level labels to train a classification branch, enabling the network to learn discriminative features and localise potential lesion regions. In the second stage, a box‐guided mask refinement strategy (BMR) is proposed, which combines Soft‐NMS filtering and a one‐to‐one matching mechanism to obtain reliable candidate regions. CIoU is further employed to derive image‐level quality metrics that impose quality‐aware weighted constraints on segmentation learning, thereby improving spatial localisation and structural consistency. In the third stage, a small number of pixel‐level labels are used for fine‐grained supervision, further enhancing segmentation accuracy and boundary details. The proposed method is validated on the BraTS 2019 and LiTS 2017 datasets, on which it outperforms several existing methods under limited annotation settings. Additional experiments on the BUSI ultrasound dataset further demonstrate its good generalisation capability across different imaging modalities.

Powered by extensive curated training data, the Segment Anything Model (SAM) demonstrates impressive generalization capabilities in open-world scenarios, effectively guided by user-provided prompts. However, the classagnostic characteristic of SAM renders its segmentation accuracy highly dependent on prompt quality. In this paper, we propose a novel plug-and-play dual-space Point Prompt Optimizer (PPO) designed to enhance prompt distribution through deep reinforcement learning (DRL)-based heterogeneous graph optimization. PPO optimizes initial prompts for any task without requiring additional training, thereby improving SAM’s downstream segmentation performance. Specifically, PPO constructs a dual-space heterogeneous graph, leveraging the robust feature-matching capabilities of a foundational pre-trained model to create internal feature and physical distance matrices. A DRL policy network iteratively refines the distribution of prompt points, optimizing segmentation predictions. We conducted experiments on four public datasets. The ablation study explores the necessity and balance of optimizing prompts in both feature and physical spaces. The comparative study shows that PPO enables SAM to surpass recent one-shot methods. Additionally, experiments with different initial prompts demonstrate PPO’s generality across prompts generated by various methods. In conclusion, PPO redefines the prompt optimization problem as a heterogeneous graph optimization task, using DRL to construct an effective, plugand-play prompt optimizer. This approach holds potential for broader applications across diverse segmentation tasks and provides a promising solution for point prompt optimization. The source code and demo are available at https://github.com/XueyuLiu/PPO.

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is time-consuming and labor-intensive. Current AI-based methods for automatic structure assessment, like segmentation, often demand extensive manual annotation and focus on single stain domain. To address these challenges, we introduce MSMTSeg, a generative self-supervised meta-learning framework for multi-stained multi-tissue segmentation in renal biopsy whole slide images (WSIs). MSMTSeg incorporates multiple stain transform models for style translation of inter-stain domains, a self-supervision module for obtaining pre-trained models with the domain-specific feature representation, and a meta-learning strategy that leverages generated virtual data and pre-trained models to learn the domain-invariant feature representation across multiple stains, thereby enhancing segmentation performance. Experimental results demonstrate that MSMTSeg achieves superior and robust performance, with mDSC of 0.836 and mIoU of 0.718 for multiple tissues under different stains, using only one annotated training sample for each stain. Our ablation study confirms the effectiveness of each component, positioning MSMTSeg ahead of classic advanced segmentation networks, recent few-shot segmentation methods, and unsupervised domain adaptation methods. In conclusion, our proposed few-shot cross-domain technology offers a feasible and cost-effective solution for multi-stained renal histology segmentation, providing convenient assistance to pathologists in clinical practice.

Multi-instance learning (MIL) exhibits advanced and surpassed capabilities in understanding and recognizing complex patterns within gigapixel histopathological images. However, the currents MIL methods for the analysis of the histopathological image still give rise to two main concerns. On one hand, vanilla MIL methods intuitively focus on identifying key instances (easy-to-classify) without considering hard-to-classify instances, which is biased and prone to produce false positive instance. On the other hand, since the positive tissue occupies only a small fraction of histopathological images, it is commonly suffer from class imbalance between positive and negative instances, causing the MIL model to overly focus on the majority class. In light of these issues of bias learning, we propose a multi-instance curriculum learning method that collaboratively incorporates hard negative instance mining and positive instance augmentation to improve model’s classification performance. Specifically, we first initialize the MIL model using easy-to-classify instances, then we mine the hard negative instances (hard-to-classify) and augment the positive instances via the diffusion model. Finally, the MIL model is retrained with memory rehearsal method by combining the mined negative instances and augmented positive instances. Technically, the diffusion model is first designed to generate lesion instances, which optimally augment diverse features to reflect the realistic positive samples with post screening scenario. Extensive experimental results show that the proposed method alleviates model bias in MIL and yields improvements over the state-of-the-art methods on both public datasets and private dataset.

Exploring proper way to conduct multi-speech feature fusion for cross-corpus speech emotion recognition is crucial as different audio features could provide complementary cues reflecting human emotion status. Speech emotion recognition allows computers to analyze the specific emotional condition of the speaker through speech, which is of great significance to the development of human–computer interaction technology. While most previous approaches only extract a single speech feature for emotion recognition, existing fusion methods such as concatenation, parallel connection, and splicing ignore heterogeneous patterns in the interaction between features and features, resulting in performance of existing systems. In this paper, we propose a novel graph-based fusion method to explicitly model the relationships between every pair of audio features, which provides a new research idea for speech feature fusion. Specifically, we propose a multi-dimensional edge features learning strategy called graph-based multi-feature fusion method for speech emotion recognition. It represents each speech feature as a node and learns multi-dimensional edge features to explicitly describe the relationship between each feature-feature pair in the context of emotion recognition. This way, the learned multi-dimensional edge features encode speech feature-level information from both the vertex and edge dimensions. Our approach consists of three modules: an Audio Feature Generation (AFG) module, an Audio-Feature Multi-dimensional Edge Feature (AMEF) module and a Speech Emotion Recognition (SER) module. The proposed methodology yielded satisfactory outcomes on the SEWA dataset. Furthermore, the method demonstrated enhanced performance compared to the baseline in the AVEC 2019 Workshop and Challenge. We used data from two cultures as our training and validation sets: two cultures containing German and Hungarian on the SEWA dataset, the CCC scores for German are improved by 17.28% for arousal and 7.93% for liking, and for Hungarian, the CCC scores are improved by 11.15% for arousal and 131.11% for valence. The outcomes of our methodology demonstrate a 13% improvement over alternative fusion techniques, including those employing one-dimensional edge-based feature fusion approach. The experiments on some parts of the Aff-Wild 2 dataset demonstrate that our approach exhibits a certain degree of generalizability and robustness. Code is available at https://github.com/ChaosWang666/Graph-based-multi-Feature-fusion-method .

Abstract With the rapid development of social economy, the incidence of breast cancer is increasing year by year. Whether there is lymph node metastasis in frozen tissue sections during breast cancer surgery is of tremendous priority for breast cancer surgical decision‐making. Therefore, it is very significant to diagnose the pathological sections of breast cancer quickly and accurately. In this study, a model which can quickly fine segmentation of lesion regions in high‐resolution breast cancer pathology sections is proposed. Firstly, pathology sections are processed by pre‐processing module; Secondly, the main lesion region in pathology sections can be quickly recognized by recognition module; Thirdly, the fine segmentation of lesion region can be accomplished by segmentation module. The dataset is selected from two medical institutions to evaluate the proposed model; it achieved the average recognition precision of 0.936 for region of interest in high‐resolution pathology section, with an F1‐score of 0.787; and the dice for lesion region segmentation is 0.8517. The proposed model outperforms several similar works, which can effectively improve the speed and precision of pathologist's diagnosis for high‐resolution breast cancer pathology sections.