Artificial Intelligence in Medicine最新文献

{"title":"Context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in renal cell carcinoma","authors":"Xiaoming Jiang ,&nbsp;Guoying Ji ,&nbsp;Ye Yan ,&nbsp;Xiongjun Ye ,&nbsp;Chao Liang ,&nbsp;Bao Li ,&nbsp;Wei Wang ,&nbsp;Shudong Zhang ,&nbsp;Lizhi Shao","doi":"10.1016/j.artmed.2025.103313","DOIUrl":"10.1016/j.artmed.2025.103313","url":null,"abstract":"<div><div>The invasiveness prediction in renal cell carcinoma (RCC) is of significant importance for the decision of clinical surgical plans and the patients' prognosis. Currently, besides invasive pathological assessment, it mainly relies on observation through computed tomography (CT) imaging. However, limitations of human vision and qualitative descriptions restrict the accuracy of the diagnosis of renal sinus invasion (RSI). Recently, artificial intelligence approaches have shown promising prospects in cancer diagnosis. Due to the complex imaging characteristics of invasiveness, prediction models that only focus on tumor regions are inadequate, requiring comprehensive evaluation of intratumoral heterogeneity, peritumoral information, and the kidney in which the tumor resides. Therefore, in this study, we propose a context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in RCC. The superiority of the proposed model lies in its ability to integrate imaging features at multi-level, and to learn disturbance invariant features through a data-driven diffusion perturbation strategy. To evaluate the effectiveness and generalization of our model, we conduct extensive experiments on a multi-center dataset (including CT scan images of 437 patients) to compare our model with a series of state-of-the-art (SOTA) classification models. The experimental results show the superiority of our model for RSI classification (<span><math><mi>AUC</mi><mo>=</mo><mn>0.88</mn></math></span>). Additionally, we also perform a comparative study with clinical experts, and the proposed method is significantly better than existing assessment methods and clinical experts (<span><math><mi>p</mi><mo>&lt;</mo><mn>0.05</mn></math></span>). In general, our work provides an effective assessment tool for automated diagnosis of RSI in RCC and also offers new insights for constructing more precise tumor prediction models.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"172 ","pages":"Article 103313"},"PeriodicalIF":6.2,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A labeled ophthalmic ultrasound dataset with medical report generation based on cross-modal deep learning","authors":"Jing Wang ,&nbsp;Junyan Fan ,&nbsp;Meng Zhou ,&nbsp;Yanzhu Zhang ,&nbsp;Mingyu Shi","doi":"10.1016/j.artmed.2025.103317","DOIUrl":"10.1016/j.artmed.2025.103317","url":null,"abstract":"<div><div>Ultrasound imaging reveals eye morphology and aids in diagnosing and treating eye diseases. However, interpreting diagnostic reports requires specialized physicians. We present a labeled ophthalmic dataset for the precise analysis and the automated exploration of medical images along with their associated reports. It collects three modal data, including the ultrasound images, blood flow information and examination reports from 1,0361 patients at an ophthalmology hospital in Shenyang, China, during the year 2016 to 2020, in which the patient information is de-identified for privacy protection. To the best of our knowledge, it is the only ophthalmic dataset that contains the three modal information simultaneously. It incrementally consists of 2,2173 images with the corresponding free-text reports, which describe 10 typical imaging findings of intraocular diseases and the corresponding anatomical locations. Each image shows three kinds of blood flow indices at three specific arteries, i.e., nine parameter values to describe the spectral characteristics of blood flow distribution. The reports were written by ophthalmologists during the clinical care. In addition, the knowledge fusion cross modal network (KFCMN) is proposed to generate report according to the proposed dataset. The experimental results demonstrate that our dataset is suitable for training supervised models concerning cross-modal medical data.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103317"},"PeriodicalIF":6.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedSumGraph: enhancing GraphRAG for medical QA with summarization and optimized prompts","authors":"DaeHo Kim ,&nbsp;SoYeop Yoo ,&nbsp;OkRan Jeong","doi":"10.1016/j.artmed.2025.103311","DOIUrl":"10.1016/j.artmed.2025.103311","url":null,"abstract":"<div><div>The rapid development of large language models (LLMs) has accelerated research into applying artificial intelligence (AI) to domains such as medical question answering and clinical decision support. However, LLMs face substantial limitations in medical contexts due to challenges in understanding specialized terminology, complex contextual information, hallucination issues (i.e., generating incorrect responses), and the black-box nature of their reasoning processes. To address these issues, methods like retrieval-augmented generation (RAG) and its graph-based variant, GraphRAG, have been proposed to incorporate external knowledge into LLMs. Nonetheless, these approaches often rely heavily on external resources and increase system complexity. In this study, we introduce MedSumGraph, a medical question-answering system that enhances GraphRAG by integrating structured medical knowledge summaries and optimized prompt designs. Our method enables LLMs to better interpret domain-specific knowledge without requiring additional training, and it enhances the reliability and interpretability of responses by directly embedding factual evidence and graph-based reasoning into the generation process. MedSumGraph achieves competitive performance on two out of eight multiple-choice medical QA benchmarks, including MedQA (USMLE), outperforming closed-source LLMs and domain-specific foundation models. Moreover, it generalizes effectively to open-domain QA tasks, yielding significant gains in reasoning over common knowledge and evaluating the truthfulness of answers. These findings demonstrate the potential of structured summarization and graph-based reasoning in enhancing the trustworthiness and versatility of LLM-driven medical AI systems.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"172 ","pages":"Article 103311"},"PeriodicalIF":6.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do machine learning methods make better predictions than conventional ones in pharmacoepidemiology? A systematic review, meta-analysis, and network meta-analysis","authors":"Ana Paula Bruno Pena-Gralle ,&nbsp;Mireille E. Schnitzer ,&nbsp;Sofia-Nada Boureguaa ,&nbsp;Félix Morin ,&nbsp;Marc-André Legault ,&nbsp;Caroline Sirois ,&nbsp;Alice Dragomir ,&nbsp;Lucie Blais","doi":"10.1016/j.artmed.2025.103312","DOIUrl":"10.1016/j.artmed.2025.103312","url":null,"abstract":"<div><h3>Objective</h3><div>To synthesize existing evidence and compare the predictive performance of conventional statistical (CS) models versus machine learning (ML) methods in pharmacoepidemiology.</div></div><div><h3>Methods</h3><div>Medline, Embase, PsycINFO, CINAHL and Web of Science databases were systematically searched for predictive pharmacoepidemiologic studies published between January 2018 and September 2025. Independent reviewers extracted predictive metrics and other data from each study and assessed the quality of the comparison between methods. The relative performance of ML compared to CS was estimated for each prediction objective. Performance metrics were pooled in meta-analyses and Bayesian network meta-analyses (NMA).</div></div><div><h3>Results</h3><div>Among 9106 records identified, 65 studies met inclusion criteria, encompassing 83 prediction objectives. For 84 % of these objectives, CS was outperformed by at least one ML method. The median sample size across these studies was 2691 subjects (50 to 1,807,159), and, for binary outcomes, the median number of events per candidate predictor was 17.9 (range 0.28 to 24,260). We observed a risk of bias in the comparison according to at least one of eight major criteria for 39 prediction objectives (47 %).</div><div>The pooled area under the receiver-operator curve (AUC) ratio for the highest-performing ML method in studies with low risk of bias was estimated as 1.07 (95 % confidence interval 1.03–1.12) in favor of ML, but with very high heterogeneity. NMA of 197 comparisons estimated an AUC ratio of 1.07 (95 % credible interval 1.04–1.12) for boosted methods compared with logistic regression and ranked Gradient Boosting Machine and XGBoost consistently among the best-performing methods.</div></div><div><h3>Conclusion</h3><div>Machine learning methods applied to structured pharmacoepidemiologic data demonstrated a consistent yet modest advantage in discriminative performance relative to conventional statistical models. This advantage was most evident for boosted methods such as GBM and XGBoost. However, greater rigor in reporting methodological details is recommended to improve the comprehension, transparency, and reproducibility of studies.</div></div><div><h3>Registration</h3><div>PROSPERO 2023 registration number: CRD42023426986.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103312"},"PeriodicalIF":6.2,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building trustworthy large language model-driven generative recommender system for healthcare decision support: A scoping review of corpus sources, customization techniques, and evaluation frameworks","authors":"Shuqi Yang ,&nbsp;Mingrui Jing ,&nbsp;Shuai Wang ,&nbsp;Zongan Huang ,&nbsp;Jiaqing Wang ,&nbsp;Jiaxin Kou ,&nbsp;Manfei Shi ,&nbsp;Zhentao Xia ,&nbsp;Qipeng Wei ,&nbsp;Weijie Xing ,&nbsp;Yan Hu ,&nbsp;Zheng Zhu","doi":"10.1016/j.artmed.2025.103310","DOIUrl":"10.1016/j.artmed.2025.103310","url":null,"abstract":"<div><h3>Introduction</h3><div>Large Language Model-Driven Generative Recommender Systems (LLM-GRSs) are playing a growing role in healthcare, particularly in clinical question-answering. This study reviews their corpus sources, customization techniques, and evaluation metrics.</div></div><div><h3>Methods</h3><div>We conducted a systematic search of PubMed, Embase, Scopus, and Web of Science for studies published between January 2021 and August 2025 that applied LLM-GRSs to deliver medical or healthcare information. Eligible studies included publications describing LLMs designed to emulate clinical decision-making by providing diagnostic or therapeutic recommendations through dialogue-based interfaces. Two reviewers independently screened studies and extracted data on corpus sources, model architectures, customization methods, and evaluation metrics.</div></div><div><h3>Results</h3><div>A total of 61 articles were included. Corpus sources were grouped into clinical data (n = 25), literature (n = 34), open datasets (n = 37), and web-crawled data (n = 15), with many using multiple types. Most studies (n = 43) combined multiple approaches. Customization techniques included prompt engineering, retrieval-augmented generation and model fine-tuning. Twenty-four studies used a single customization technique, while 37 studies combined these methods during model development. The evaluation metrics were classified into three main domains: process metrics, usability metrics, and outcome metrics. The outcome metrics included both model-based and manual-assessed evaluations.</div></div><div><h3>Conclusion</h3><div>LLM-GRSs hold considerable promise in healthcare; however, their safety and reliability hinge on the use of evidence-based training corpora, transparent system design, and standardized evaluation protocols within real-world clinical environments.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103310"},"PeriodicalIF":6.2,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CA-OCL and CHAN: A novel diagnostic framework for rheumatoid arthritis integrating contradiction-aware orthogonal contrastive learning with confidence-guided hierarchical attention","authors":"Zhao Huang ,&nbsp;QingMei Zeng ,&nbsp;NanNan Gai","doi":"10.1016/j.artmed.2025.103307","DOIUrl":"10.1016/j.artmed.2025.103307","url":null,"abstract":"<div><div>Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive destruction of synovial joints, for which precise early diagnosis is critical to effective clinical management. Although current computer-aided diagnosis (CAD) systems show promising potential, their practical deployment remains challenged by heterogeneity in multimodal data and the inherent complexity of pathophysiological manifestations. A key limitation of existing approaches is the absence of dedicated mechanisms to reconcile inter-modal conflicts and to adequately leverage the diagnostic information present in contradictory samples, such as discordances between laboratory findings and clinical descriptions, which often leads to suboptimal diagnostic performance. To overcome these challenges, this study introduces a novel auxiliary diagnostic framework for RA based on Contradiction-Aware Orthogonal Contrastive Learning (CA-OCL) and a Confidence-guided Hierarchical Attention Network (CHAN). The proposed architecture incorporates three major innovations. First, in the data processing stage, a lightweight feature-crossing network is employed to derive robust representations from structured data, while a multi-task adapted extension of the BERT model is utilized to extract rich semantic features from unstructured textual inputs. Second, the CA-OCL module is designed to explicitly identify and learn from contradictory negative samples—a capability largely absent in conventional contrastive learning frameworks. Additionally, orthogonal constraints are applied to minimize feature redundancy across modalities, thereby preserving discriminative modality-specific information that is often obscured by methods promoting excessive feature alignment. Finally, the CHAN module dynamically modulates inter-modal contributions using confidence estimates, mitigating the risk of unilateral dominance by any single modality, a common drawback in attention-based fusion mechanisms and facilitating refined integration of conflicting information. Comprehensive experimental evaluations demonstrate that the proposed framework achieves superior performance compared to state-of-the-art methods. These results not only validate the efficacy of our approach in handling multimodal conflicts and exploiting contradictory evidence, but also highlight its significant clinical utility through effective multimodal data integration. This work addresses critical limitations in conventional CAD systems and provides an advanced paradigm for intelligent diagnostic assessment of RA.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103307"},"PeriodicalIF":6.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in 4D flow MRI: Review of technological aspects and clinical applications","authors":"Jiwon Pung ,&nbsp;Gyu-Han Lee ,&nbsp;Hyungkyu Huh ,&nbsp;Dong Hyun Yang ,&nbsp;Hojin Ha","doi":"10.1016/j.artmed.2025.103308","DOIUrl":"10.1016/j.artmed.2025.103308","url":null,"abstract":"<div><h3>Background</h3><div>Four-dimensional (4D) flow magnetic resonance imaging (MRI) has evolved into an advanced non-invasive imaging technique that enables comprehensive assessment of blood flow in cardiovascular system. Hemodynamic data from 4D flow MRI provide key biomarkers such as wall shear stress (WSS), turbulent kinetic energy (TKE), and viscous energy loss, which aid in the analysis of cardiovascular diseases. However, its clinical application is limited by challenges such as prolonged scan times and limited spatiotemporal resolution. Conventional algorithms have been developed to automate the reconstruction of sparse data and perform image segmentation for hemodynamic quantification, but these methods are often time-consuming and require user expertise for accurate postprocessing. To address these limitations, artificial intelligence (AI), particularly deep learning (DL) techniques, has been introduced. DL models have shown promise in accelerating scan times by reconstructing sparsely sampled data into fully sampled datasets and enhancing image resolution by combining computational fluid dynamics (CFD) with 4D flow MRI data, as well as improving data quality through noise reduction. In addition, automated segmentation techniques have been developed to reduce user intervention, enabling more consistent and efficient analysis. Many researchers are working on DL-based approaches to 4D flow MRI using limited datasets. Recently, the lack of systematic methodologies has made it difficult to identify appropriate approaches. This paper aims to provide a comprehensive review of the latest AI applications using 4D flow MRI, enabling researchers to access and evaluate existing research more effectively.</div></div><div><h3>Conclusions</h3><div>This review explores the latest research on integrating AI with 4D flow MRI, covering the entire process from data acquisition to postprocessing, and emphasizes the need for novel AI-based techniques to enhance clinical applicability. Furthermore, this review suggests it can serve as a foundation for developing innovative strategies toward fully automated AI-based approaches.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103308"},"PeriodicalIF":6.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weakly-supervised ultrasound image segmentation with elliptical shape prior constraint","authors":"Changyan Wang ,&nbsp;Yehua Cai ,&nbsp;Ruyi Yang ,&nbsp;Haobo Chen ,&nbsp;Jiang Shang ,&nbsp;Hong Ding ,&nbsp;Qi Zhang","doi":"10.1016/j.artmed.2025.103306","DOIUrl":"10.1016/j.artmed.2025.103306","url":null,"abstract":"<div><div>Accurate pixel-level segmentation of ultrasound (US) images is vital for computer-aided disease screening, diagnosis, and treatment response evaluation. The weakly supervised methods have the potential to reduce the time-consuming and labor-intensive workload for radiologists, paving the way for further automation in the quantitative analysis of US images. Among these methods, the multiple instance learning (MIL) has proven effective and is often applied to prediction tasks with insufficiently labeled data. In US examinations, the elliptical region formed by intersecting lines used by radiologists for target annotation serves as a crucial prior information. Therefore, we propose a novel weakly supervised method called elliptical shape prior constraint MIL (ESPC-MIL) for pixel-level segmentation of US images. ESPC-MIL incorporates an elliptical shape prior constraint into the MIL framework, delivering more accurate foreground and background candidate regions for MIL, which enhances its predictive performance for tissues and organs with approximately elliptical shapes. Furthermore, the method utilizes elliptical shape prior information for global supervision, improving edge segmentation and localization accuracy. Compared to other weakly supervised methods, ESPC-MIL achieves state-of-the-art results on four US image datasets: Achilles tendon dataset, median nerve dataset, private breast tumor dataset, and public breast ultrasound image dataset, with Dice similarity coefficients of 0.855, 0.849, 0.876, and 0.748, respectively. It demonstrates performance comparable to fully supervised segmentation methods while significantly reducing annotation requirements. Notably, the method demonstrates a more significant performance improvement in segmenting objects with approximately elliptical shapes compared to those with complex shapes. Source codes and models are available at <span><span>https://github.com/CYWang-kayla/ESPC-MIL-Model</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103306"},"PeriodicalIF":6.2,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformable phrase level attention: A flexible approach for improving AI based medical coding","authors":"Christoph Metzner ,&nbsp;Shang Gao ,&nbsp;Drahomira Herrmannova ,&nbsp;John Gounley ,&nbsp;Heidi A. Hanson","doi":"10.1016/j.artmed.2025.103299","DOIUrl":"10.1016/j.artmed.2025.103299","url":null,"abstract":"<div><h3>Objective:</h3><div>Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel attention mechanism designed to enhance text classification models and ensure appropriate classification of medical concepts in unstructured electronic health records.</div></div><div><h3>Materials and Methods:</h3><div>We developed a deformable, phrase-level attention mechanism to identify important lexical word-level and contextual phrase-level information from clinical text documents. We evaluated conventional and transformer-based deep learning models that we extended with our attention mechanism on the extraction of critical cancer information (e.g., site, subsite, laterality, histology, behavior) from 629,908 electronic pathology reports and on the automated medical encoding of 52,722 hospital discharge summaries.</div></div><div><h3>Results:</h3><div>Transformer-based models with the deformable, phrase-level attention mechanism achieved the best performance on the extraction of critical cancer information from pathology reports. Conventional- and transformer-based models show similar or better performance than their baseline counterparts on the automated medical encoding of clinical documents.</div></div><div><h3>Discussion:</h3><div>The addition of phrase-level information allowed models extended with our proposed method to outperform standard word-level attention. Our method showed favorable properties for the real-world application in terms of model robustness and phenotyping. These results indicate that our method is promising for automated data harmonization for common data models.</div></div><div><h3>Conclusion:</h3><div>This work proposes a novel deformable, phrase-level attention mechanism that enhances text classification models in the extraction of medical concepts from clinical text documents. We demonstrate strong performances on two clinical text datasets and showcase real-world deployability of our method.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103299"},"PeriodicalIF":6.2,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep representation learning algorithm on drug-target interaction to screen novel drug candidates for Alzheimer's disease","authors":"Xin Yuan ,&nbsp;Lihui Gao ,&nbsp;Yonglin Peng ,&nbsp;Tiantian She ,&nbsp;Ju Wang","doi":"10.1016/j.artmed.2025.103301","DOIUrl":"10.1016/j.artmed.2025.103301","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a serious neurodegenerative brain disorder with complex pathophysiology. While currently available drugs can provide symptomatic benefits, they often fail to cure the disease. Thus, there is an urgent need to explore new therapeutic agents. In this study, we developed DTIP (Drug-Target Interaction Prediction), a machine learning-based approach to search novel drugs for AD by utilizing the information of drug-target interaction (DTI). By training a Skip-gram model on drug-target sequences derived from known DTI information, the algorithm learned the drug-target relationship embeddings and to predict potential drug candidates for diseases like AD. For AD, we compiled 917 risk genes and identified 292 potential drugs via the new algorithm. We further performed molecular docking by AutoDock Vina and conducted Inverted Gene Set Enrichment Analysis (IGSEA) on these drug candidates. Our results identified that several drugs could be promising for AD treatment, including human C1-esterase inhibitor, quetiapine, dasatinib, miconazole, aniracetam, chlorpromazine, hypericin, entrectinib, torcetrapib, bosutinib, sunitinib, aniracetam, rosiglitazone, tarenflurbil, milrinone, and MITO-4509. Results from this study also provided insights for understanding the molecular mechanisms underlying AD. As a systematic and versatile method, our approach can also be applied to identify efficacious therapies for other complex diseases.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"171 ","pages":"Article 103301"},"PeriodicalIF":6.2,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}