Journal of Biomedical Informatics最新文献

{"title":"Uncovering digital overdiagnosis - Quantification and mitigation using clinical trajectories: Heparin-induced thrombocytopenia use case.","authors":"Prabodi Senevirathna, Douglas E V Pires, Daniel Capurro","doi":"10.1016/j.jbi.2025.104876","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104876","url":null,"abstract":"<p><strong>Objective: </strong>Overdiagnosis occurs when abnormalities meeting diagnostic criteria would remain asymptomatic if undiagnosed. Cases initially identified through digital diagnostic tools but later recognised as overdiagnosis are referred to as 'digital overdiagnosis'. Data-driven frameworks to quantify and mitigate overdiagnosis remain limited. This study introduces a framework that integrates clinical trajectories to train a machine learning (ML)-based disease classifier, enabling the quantification and mitigation of digital overdiagnosis, using Heparin-Induced Thrombocytopenia (HIT) as a case study.</p><p><strong>Methods: </strong>A pre-existing HIT classifier identified HIT-positive and HIT-negative cases, with ground truth based on HIT diagnostic criteria. Clinical trajectories for True Positive (TP) and True Negative (TN) patients were clustered using a novel process-models-based approach. Overdiagnosis was detected when TP cases clustered with predominantly TN cases. The classifier was then retrained with an 'updated label' integrating both HIT criteria and the concordant trajectory, to reduce overdiagnosis while maintaining accuracy.</p><p><strong>Results: </strong>7.2% of TP cases were identified as overdiagnosed. Retraining with the updated labels successfully reclassified 89.5% of overdiagnosed cases as TN, with only a minimal reduction in performance (MCC decreased by 0.03, positive likelihood ratio decreased by 0.49, and negative likelihood ratio increased by 0.05). Clinical outcomes-length of stay, thrombotic events, and mortality-differed significantly between non-overdiagnosed and overdiagnosed cases, and between non-overdiagnosed and TN cases, but not between overdiagnosed and TN cases, confirming that overdiagnosed patients resemble TN patients.</p><p><strong>Conclusion: </strong>Incorporating clinical trajectories into ML-based diagnosis enables the quantification of digital overdiagnosis. This approach could refine ML algorithms by prompting a reassessment of criteria-based disease labels in supervised learning.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104876"},"PeriodicalIF":4.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608513","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":"MedKA: A knowledge graph-augmented approach to improve factuality in medical Large Language Models","authors":"Yiyan Deng ,&nbsp;Shen Zhao ,&nbsp;Yongming Miao ,&nbsp;Junjie Zhu ,&nbsp;Jin Li","doi":"10.1016/j.jbi.2025.104871","DOIUrl":"10.1016/j.jbi.2025.104871","url":null,"abstract":"<div><div>Large language models (LLMs) have demonstrated remarkable potential in medical applications. However, they still face critical challenges such as hallucinations, knowledge inconsistency, and insufficient integration of domain-specific medical expertise. To address these limitations, we introduce MedKA, a novel knowledge graph-augmented approach for fine-tuning and evaluating medical LLMs. Our approach systematically transforms structured knowledge from a medical knowledge graph into a high-quality QA corpus, cMKGQA, by clustering multiple fields around clinically meaningful scenarios (e.g., diagnosis, treatment planning). This grouping strategy enables comprehensive and use-case-specific data construction and supports one-stage training of the LLM, ensuring better alignment with structured medical knowledge. This transformation process ensures the comprehensive integration of domain-specific knowledge while maintaining factual consistency. To evaluate the factuality of LLM-generated response, we further propose the Knowledge Graph-based Auxiliary Evaluation Metrics (KG-AEMs)—a novel benchmarking framework that compares LLM outputs with fine-grained, attribute-level ground truth from knowledge graph. Experimental results demonstrate that MedKA achieves state-of-the-art performance, significantly outperforming existing models, including LLaMA-3.1-8B-Chinese-Chat, HuatuoGPT2-7B, and Apollo2-7B. On the cMKGQA dataset, MedKA achieves 44.63 BLEU-1 and 17.62 BLEU-4 scores, with particularly strong performance in areas such as medication recommendations and diagnostic tests as measured by KG-AEMs. Our approach highlights the potential of integrating knowledge graphs into LLM fine-tuning to improve the accuracy and reliability of medical AI systems. It advances factual accuracy in medical dialogue systems and provides a comprehensive framework for evaluating the integration of medical knowledge into LLMs. This work is publicly available on Github: <span><span>https://github.com/Yai017/MedKA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"168 ","pages":"Article 104871"},"PeriodicalIF":4.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596438","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":"Joint modeling of mixed outcomes using a rank-based sparse neural network.","authors":"Jiajing Xue, Yaqing Xu, Jingmao Li, Shuangge Ma, Kuangnan Fang","doi":"10.1016/j.jbi.2025.104870","DOIUrl":"10.1016/j.jbi.2025.104870","url":null,"abstract":"<p><strong>Objective: </strong>In the past few decades, high-throughput profiling has been extensively conducted, leading to significant advancements in cancer research, survival analysis, and other biomedical studies. While many methods have been developed to identify important features and construct predictive models, biomedical research often faces challenges due to insufficient information caused by high dimensionality and small sample sizes, which frequently lead to unsatisfactory identification and prediction accuracy.</p><p><strong>Methods: </strong>In this paper, we propose a rank-based sparse neural network that efficiently leverages information from mixed outcomes, particularly incorporating survival data. The proposed method accounts for unknown relationships between outcomes and high-dimensional covariates, whereas many traditional methods are built on a parametric framework. A novel loss function is derived to address the gradient imbalance issue and accommodate mixed outcomes. A sparse layer is developed to implement the penalization method, enabling the identification of important variables.</p><p><strong>Results: </strong>We conducted extensive simulation studies, showing that the proposed method is effective and broadly applicable. The analysis of skin cutaneous melanoma (SKCM) demonstrates the competitive performance of our proposed method.</p><p><strong>Conclusion: </strong>The proposed method effectively models mixed outcomes (including survival data) and selects important features, which is beneficial for biomedical studies like cancer and genomic research.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104870"},"PeriodicalIF":4.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584025","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":"Accounting for population structure in deep learning models for genomic analysis.","authors":"Gabrielle Dagasso, Matthias Wilms, Raissa Souza, Nils D Forkert","doi":"10.1016/j.jbi.2025.104873","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104873","url":null,"abstract":"<p><strong>Background: </strong>Deep learning methods are becoming increasingly popular for genotype analyses in recent years. In conventional genomic analyses, it is important to account for confounders to avoid biasing the results. Genetic relatedness is one of the most common confounders in conventional genomic analyses and there is a general consensus that it should be considered in the analysis to prevent distant levels of common ancestry from affecting the identification of causal variants. In contrast, genetic relatedness is not considered or ignored in many of the recently published deep learning models.</p><p><strong>Objective: </strong>This study investigates whether the omission of genetic relatedness in deep learning models, common in recent literature, introduces confounding effects similar to those observed in conventional genomic analyses, particularly due to ancestry-related variants.</p><p><strong>Methods: </strong>We developed and used a deep learning model to perform classifications based on single nucleotide polymorphism data from simulated and real-world datasets to examine whether population structure is confounding the model and potentially causing shortcut learning.</p><p><strong>Results: </strong>The results of this study suggest that population structure may not significantly affect the performance of the deep learning model. However, explainable AI revealed notable differences in the focus between the confounded and unconfounded models when examining SNP feature importance.</p><p><strong>Conclusion: </strong>While population structure may not heavily affect model performance, it is important to reduce the models' capabilities of shortcut learning when designing deep learning models for analyzing genomic datasets, by using ancestry-related variants over potentially relevant biomarkers of the disease or disorder in question. The code used to perform these analyses can be found at: https://github.com/notTrivial/populationStructure.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104873"},"PeriodicalIF":4.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584024","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":"Leveraging heterogeneous tabular of EHRs with prompt learning for clinical prediction","authors":"Xuebing Yang ,&nbsp;Longyu Li ,&nbsp;Chutong Wang ,&nbsp;Wensheng Zhang ,&nbsp;Huizhou Liu ,&nbsp;Wen Tang","doi":"10.1016/j.jbi.2025.104868","DOIUrl":"10.1016/j.jbi.2025.104868","url":null,"abstract":"<div><div>Electronic Health Records (EHRs) depict patient-related information and have significantly contributed to advancements in healthcare fields. The abundance of EHR data provides exceptional opportunities for developing clinical predictive models. However, the heterogeneity within multi-source EHR data raises a difficulty to organically leverage information from structured and unstructured features. In this paper, we focus on the heterogeneous EHR data in the tabular form, and propose a Prompt learning based data Fusion framework for Tabular (TabPF) to extract patient representations for clinical prediction. First, we design a text summary generator module to convert medical tabular into vector representations through long text embedding. Specifically, the tailored prompt learning is conducted for leading the Large Language Model (LLM) to respectively generate appropriate text summaries for different types of tabular data. Second, we design a novel attention mechanism of Transformer to effectively realize heterogeneous data fusion and generate more comprehensive patient representations for downstream predictions. The experiments are performed on the publicly available eICU-CRD dataset and the real-world CECMed dataset containing elderly patients diagnosed with chronic diseases, in comparison with representative baseline models. The results validate the superior performance of TabPF in predicting severity, mortality and Length of Stay (LoS). Furthermore, extensive ablation study and model variants evaluations demonstrate the effectiveness of the key component of the proposed framework.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"168 ","pages":"Article 104868"},"PeriodicalIF":4.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557652","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":"How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analyses","authors":"Qingqing Zhu ,&nbsp;Benjamin Hou ,&nbsp;Tejas Sudarshan Mathai ,&nbsp;Pritam Mukherjee ,&nbsp;Qiao Jin ,&nbsp;Xiuying Chen ,&nbsp;Zhizheng Wang ,&nbsp;Ruida Cheng ,&nbsp;Ronald M. Summers ,&nbsp;Zhiyong Lu","doi":"10.1016/j.jbi.2025.104864","DOIUrl":"10.1016/j.jbi.2025.104864","url":null,"abstract":"<div><h3>Objective:</h3><div>This study introduces a novel evaluation framework, <em>GPTRadScore</em>, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore leverages LLMs as an evaluation metric, aiming to provide a more accurate and clinically informed assessment than traditional language-specific methods. Using this framework, we evaluate the capability of several MLLMs, including GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, to interpret findings in CT scans.</div></div><div><h3>Methods:</h3><div>This retrospective study leverages a subset of the public DeepLesion dataset to evaluate the performance of several multimodal LLMs in describing findings in CT slices. <em>GPTRadScore</em> was developed to assess the generated descriptions (location, body part, and type) using GPT-4, alongside traditional metrics. RadFM was fine-tuned using a subset of the DeepLesion dataset with additional labeled examples targeting complex findings. Post fine-tuning, performance was reassessed using <em>GPTRadScore</em> to measure accuracy improvements.</div></div><div><h3>Results:</h3><div>Evaluations demonstrated a high correlation of <em>GPTRadScore</em> with clinician assessments, with Pearson’s correlation coefficients of 0.87, 0.91, 0.75, 0.90, and 0.89. These results highlight its superiority over traditional metrics, such as BLEU, METEOR, and ROUGE, and indicate that GPTRadScore can serve as a reliable evaluation metric. Using <em>GPTRadScore</em>, it was observed that while GPT-4V and Gemini Pro Vision outperformed other models, significant areas for improvement remain, primarily due to limitations in the datasets used for training. Fine-tuning RadFM resulted in substantial accuracy gains: location accuracy increased from 3.41% to 12.8%, body part accuracy improved from 29.12% to 53%, and type accuracy rose from 9.24% to 30%. These findings reinforce the hypothesis that fine-tuning RadFM can significantly enhance its performance.</div></div><div><h3>Conclusion:</h3><div>GPT-4 effectively correlates with expert assessments, validating its use as a reliable metric for evaluating multimodal LLMs in radiological diagnostics. Additionally, the results underscore the efficacy of fine-tuning approaches in improving the descriptive accuracy of LLM-generated medical imaging findings.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"168 ","pages":"Article 104864"},"PeriodicalIF":4.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511103","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 comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients","authors":"Yiming Li ,&nbsp;Fang Li ,&nbsp;Na Hong ,&nbsp;Manqi Li ,&nbsp;Kirk Roberts ,&nbsp;Licong Cui ,&nbsp;Cui Tao ,&nbsp;Hua Xu","doi":"10.1016/j.jbi.2025.104867","DOIUrl":"10.1016/j.jbi.2025.104867","url":null,"abstract":"<div><h3>Objective</h3><div>Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings.</div></div><div><h3>Materials and methods</h3><div>Clinical notes from a cohort of 1,099 lung cancer patients were utilized, with a subset of 50 patients for testing purposes, and 102 patients used for model fine-tuning. This study evaluates the performance of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, and LLaMA 3 8b, in generating discharge summaries. Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L), semantic similarity scores, and manual evaluation of clinical relevance, factual faithfulness, and completeness. An iterative method was further tested on LLaMA 3 8b using clinical notes of varying lengths to examine the stability of its performance.</div></div><div><h3>Results</h3><div>The study found notable variations in summarization capabilities among LLMs. GPT-4o and fine-tuned LLaMA 3 demonstrated superior token-level evaluation metrics, while manual evaluation further revealed that GPT-4 achieved the highest scores in relevance (4.95 ± 0.22) and factual faithfulness (4.40 ± 0.50), whereas GPT-4o performed best in completeness (4.55 ± 0.69); both models showed comparable overall quality. Semantic similarity scores indicated GPT-4o and LLaMA 3 as leading models in capturing the underlying meaning and context of clinical narratives.</div></div><div><h3>Conclusion</h3><div>This study contributes insights into the efficacy of LLMs for generating discharge summaries, highlighting the potential of automated summarization tools to enhance documentation precision and efficiency, ultimately improving patient care and operational capability in healthcare settings.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"168 ","pages":"Article 104867"},"PeriodicalIF":4.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144368889","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":"KGiA: Drug repurposing through disease-aware knowledge graph augmentation","authors":"Çerağ Oğuztüzün ,&nbsp;Zhenxiang Gao ,&nbsp;Hui Li ,&nbsp;Rong Xu","doi":"10.1016/j.jbi.2025.104857","DOIUrl":"10.1016/j.jbi.2025.104857","url":null,"abstract":"<div><h3>Objective:</h3><div>Drug repurposing offers a cost-effective strategy to accelerate drug development by identifying new therapeutic uses for approved medications. Knowledge graphs (KGs) that capture large amounts of biomedical knowledge have recently been used for drug repurposing, however, KGs are inherently incomplete due to our limited biomedical knowledge.</div></div><div><h3>Methods:</h3><div>We propose KGiA, an inductive graph augmentation method that supports semi-inductive reasoning—allowing models to generalize to previously unseen biomedical entities. KGiA enhances KGs using counterfactual relationships mined from disease-specific topological patterns. We apply it to a state-of-art biomedical KG constructed from six datasets including biomedical relationships extracted from biomedical literature, which comprised 1,614,801 triples and 100,563 entities, including 30,006 diseases.</div></div><div><h3>Results:</h3><div>Across five augmented architectures, KGiA improves generalizability by up to 24×<!--> <!-->in Mean Reciprocal Rank (MRR) and outperforms the state-of-the-art KG-based drug repurposing model by up to 32%. We applied KGiA in four case studies of diseases including Alzheimer’s Disease and showed its promise in identifying novel repurposed candidate drugs.</div></div><div><h3>Conclusion:</h3><div>We showed that leveraging counterfactual relationships derived from disease-specific graph structures to augment existing knowledge graphs improved performance in KG-based drug repurposing.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"168 ","pages":"Article 104857"},"PeriodicalIF":4.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364743","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":"Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement","authors":"Navya Martin Kollapally ,&nbsp;James Geller ,&nbsp;Vipina Kuttichi Keloth ,&nbsp;Zhe He ,&nbsp;Julia Xu","doi":"10.1016/j.jbi.2025.104865","DOIUrl":"10.1016/j.jbi.2025.104865","url":null,"abstract":"<div><h3>Objective</h3><div>Ontologies are essential for representing the knowledge of a domain. To make ontologies useful, they must encompass a comprehensive domain view. To achieve ontology enrichment, there is a need to discover new concepts to be added, either because they were missed in the first place, or the state-of-the-art has advanced to develop new real-world concepts. Our goal is to develop an automatic enrichment pipeline using a seed ontology, a Large Language Model (LLM), and source of text. The pipeline is applied to the domain of Social Determinants of Health (SDoH), using PubMed as a source of concepts. In this work, the applicability and effectiveness of the enrichment pipeline is demonstrated by extending the SDoH Ontology called SOHOv1, however our methodology could be used in other domains as well.</div></div><div><h3>Methods</h3><div>We first retrieved PubMed abstracts of candidate articles with existing SOHOv1 concepts as search terms. Next, we used GPT-4-1201 to extract semantic triples from the abstracts. We identified concepts from these triples utilizing lexical, semantic, and knowledge network-based filtering. We also compared the granularity of semantic triples extracted with our method to the triples in the SemMedDB (Semantic MEDLINE Database). The results were evaluated by human experts and standard ontology tools for checking consistency and semantic correctness.</div></div><div><h3>Results</h3><div>We expanded SOHOv1, which contained 173 concepts and 585 axioms, including 207 logical axioms to SOHOv2, which contains 572 concepts, 1,542 axioms, including 725 logical axioms. Our methods identified more concepts than those extracted from SemMedDB for the same task. While we have shown the feasibility of our approach for an SDoH ontology, the methodology is generalizable to other ontologies with an existing seed ontology and text corpus.</div></div><div><h3>Conclusions</h3><div>The contributions of this work are: Extracting semantic triples from PubMed abstracts using GPT-4-1201 utilizing <em>prompt chaining</em>; showing the superiority of triples from GPT-4-1201 over triples from SemMedDB for SDoH; using lexical and semantic similarity search techniques with knowledge network-based search to identify the concepts to be added to the ontology; confirming the quality of the new concepts with human experts.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"168 ","pages":"Article 104865"},"PeriodicalIF":4.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340171","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":"Corrigendum to \"Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence\" [J. Biomed. Inform. 148 (2023) 104550].","authors":"Alexander F Stevens, Pete Stetson","doi":"10.1016/j.jbi.2025.104863","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104863","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104863"},"PeriodicalIF":4.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317028","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}