Journal of the American Medical Informatics Association最新文献

{"title":"A model for supporting biomedical and public health researcher use of publicly available All of Us data at Historically Black Colleges and Universities.","authors":"Brian Southwell, Sula Hood, Javan Carter, Courtney Richardson, Sheri Cates, Hadyatoullaye Sow, MaryBeth Branigan, Trey-Rashad Hawkins, Katie Atkinson, Jennifer Uhrig, Megan Lewis","doi":"10.1093/jamia/ocae099","DOIUrl":"10.1093/jamia/ocae099","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to describe opportunities and challenges associated with the development and implementation of a program for supporting researchers underrepresented in biomedical research.</p><p><strong>Approach: </strong>We describe a case study of the All of Us Researcher Academy supported by the National Institutes of Health (NIH), including feedback from participants, instructors, and coaches.</p><p><strong>Findings: </strong>Lessons include the importance of inviting role models into learning networks, establishing and maintaining trusted relationships, and making coaches available for technical questions from researcher participants.</p><p><strong>Originality: </strong>Although research has focused on learning outcomes in science, technology, engineering, and mathematics at Minority Serving Institutions in the United States, literature tends to lack models for initiatives to improve everyday research experiences of faculty and researchers at such institutions or to encourage researcher use of public-use data such as that available through NIH's All of Us Research Program. The All of Us Researcher Academy offers a model that addresses these needs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2989-2993"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program.","authors":"Kiana L Martinez, Andrew Klein, Jennifer R Martin, Chinwuwanuju U Sampson, Jason B Giles, Madison L Beck, Krupa Bhakta, Gino Quatraro, Juvie Farol, Jason H Karnes","doi":"10.1093/jamia/ocae161","DOIUrl":"10.1093/jamia/ocae161","url":null,"abstract":"<p><strong>Objectives: </strong>ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature.</p><p><strong>Materials and methods: </strong>Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program.</p><p><strong>Results: </strong>Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23).</p><p><strong>Discussion: </strong>Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals.</p><p><strong>Conclusion: </strong>Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3022-3031"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research.","authors":"Louisa H Smith, Wanjiang Wang, Brianna Keefe-Oates","doi":"10.1093/jamia/ocae195","DOIUrl":"10.1093/jamia/ocae195","url":null,"abstract":"<p><strong>Objectives: </strong>The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. Pregnant individuals have historically been underrepresented in biomedical research, and pregnancy-related research is often limited by data availability, sample size, and inadequate representation of the diversity of pregnant people. All of Us integrates a wealth of health-related data, providing a unique opportunity to conduct comprehensive pregnancy-related research. We aimed to identify pregnancy episodes with high-quality electronic health record (EHR) data in All of Us Research Program data and evaluate the program's utility for pregnancy-related research.</p><p><strong>Materials and methods: </strong>We used a previously published algorithm to identify pregnancy episodes in All of Us EHR data. We described these pregnancies, validated them with All of Us survey data, and compared them to national statistics.</p><p><strong>Results: </strong>Our study identified 18 970 pregnancy episodes from 14 234 participants; other possible pregnancy episodes had low-quality or insufficient data. Validation against people who reported a current pregnancy on an All of Us survey found low false positive and negative rates. Demographics were similar in some respects to national data; however, Asian-Americans were underrepresented, and older, highly educated pregnant people were overrepresented.</p><p><strong>Discussion: </strong>Our approach demonstrates the capacity of All of Us to support pregnancy research and reveals the diversity of the pregnancy cohort. However, we noted an underrepresentation among some demographics. Other limitations include measurement error in gestational age and limited data on non-live births.</p><p><strong>Conclusion: </strong>The wide variety of data in the All of Us program, encompassing EHR, survey, genomic, and fitness tracker data, offers a valuable resource for studying pregnancy, yet care must be taken to avoid biases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2789-2799"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LCD benchmark: long clinical document benchmark on mortality prediction for language models.","authors":"WonJin Yoon, Shan Chen, Yanjun Gao, Zhanzhan Zhao, Dmitriy Dligach, Danielle S Bitterman, Majid Afshar, Timothy Miller","doi":"10.1093/jamia/ocae287","DOIUrl":"10.1093/jamia/ocae287","url":null,"abstract":"<p><strong>Objectives: </strong>The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent.</p><p><strong>Materials and methods: </strong>To address this issue, we propose Long Clinical Document (LCD) benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of Medical Information Mart for Intensive Care IV and statewide death data. We evaluated this benchmark dataset using baseline models, from bag-of-words and convolutional neural network to instruction-tuned large language models. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations.</p><p><strong>Results: </strong>Baseline models showed 28.9% for best-performing supervised models and 32.2% for GPT-4 in F1 metrics. Notes in our dataset have a median word count of 1687.</p><p><strong>Discussion: </strong>Our analysis of the model outputs showed that our dataset is challenging for both models and human experts, but the models can find meaningful signals from the text.</p><p><strong>Conclusion: </strong>We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741203","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":"Health system-wide access to generative artificial intelligence: the New York University Langone Health experience.","authors":"Kiran Malhotra, Batia Wiesenfeld, Vincent J Major, Himanshu Grover, Yindalon Aphinyanaphongs, Paul Testa, Jonathan S Austrian","doi":"10.1093/jamia/ocae285","DOIUrl":"https://doi.org/10.1093/jamia/ocae285","url":null,"abstract":"<p><strong>Objectives: </strong>The study aimed to assess the usage and impact of a private and secure instance of a generative artificial intelligence (GenAI) application in a large academic health center. The goal was to understand how employees interact with this technology and the influence on their perception of skill and work performance.</p><p><strong>Materials and methods: </strong>New York University Langone Health (NYULH) established a secure, private, and managed Azure OpenAI service (GenAI Studio) and granted widespread access to employees. Usage was monitored and users were surveyed about their experiences.</p><p><strong>Results: </strong>Over 6 months, over 1007 individuals applied for access, with high usage among research and clinical departments. Users felt prepared to use the GenAI studio, found it easy to use, and would recommend it to a colleague. Users employed the GenAI studio for diverse tasks such as writing, editing, summarizing, data analysis, and idea generation. Challenges included difficulties in educating the workforce in constructing effective prompts and token and API limitations.</p><p><strong>Discussion: </strong>The study demonstrated high interest in and extensive use of GenAI in a healthcare setting, with users employing the technology for diverse tasks. While users identified several challenges, they also recognized the potential of GenAI and indicated a need for more instruction and guidance on effective usage.</p><p><strong>Conclusion: </strong>The private GenAI studio provided a useful tool for employees to augment their skills and apply GenAI to their daily tasks. The study underscored the importance of workforce education when implementing system-wide GenAI and provided insights into its strengths and weaknesses.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711185","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":"National COVID Cohort Collaborative Data Enhancements: A Path for Expanding Common Data Models.","authors":"Kellie M Walters, Marshall Clark, Sofia Dard, Stephanie S Hong, Elizabeth Kelly, Kristin Kostka, Adam M Lee, Robert T Miller, Michele Morris, Matvey B Palchuk, Emily R Pfaff","doi":"10.1093/jamia/ocae299","DOIUrl":"https://doi.org/10.1093/jamia/ocae299","url":null,"abstract":"<p><strong>Introduction: </strong>To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include: long COVID specialty clinic indicator; Admission, Discharge, and Transfer (ADT) transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation.</p><p><strong>Methods: </strong>For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.</p><p><strong>Results: </strong>As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline.</p><p><strong>Discussion: </strong>The use of common data models is critical to the success of N3C; however, these data models cannot account for all needs. Project-driven data enhancement is required. This should be done in a standardized way in alignment with CDM specifications. Our approach offers a useful pathway for enhancing data to improve fit for purpose.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696017","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":"Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables.","authors":"Meng-Han Tsai, Sung-Chu Ko, Amy Huaishiuan Huang, Lorenzo Porta, Cecilia Ferretti, Clarissa Longhi, Wan-Ting Hsu, Yung-Han Chang, Jo-Ching Hsiung, Chin-Hua Su, Filippo Galbiati, Chien-Chang Lee","doi":"10.1093/jamia/ocae286","DOIUrl":"https://doi.org/10.1093/jamia/ocae286","url":null,"abstract":"<p><strong>Objectives: </strong>To pioneer the first artificial intelligence system integrating radiological and objective clinical data, simulating the clinical reasoning process, for the early prediction of high-risk influenza patients.</p><p><strong>Materials and methods: </strong>Our system was developed using a cohort from National Taiwan University Hospital in Taiwan, with external validation data from ASST Grande Ospedale Metropolitano Niguarda in Italy. Convolutional neural networks pretrained on ImageNet were regressively trained using a 5-point scale to develop the influenza chest X-ray (CXR) severity scoring model, FluDeep-XR. Early, late, and joint fusion structures, incorporating varying weights of CXR severity with clinical data, were designed to predict 30-day mortality and compared with models using only CXR or clinical data. The best-performing model was designated as FluDeep. The explainability of FluDeep-XR and FluDeep was illustrated through activation maps and SHapley Additive exPlanations (SHAP).</p><p><strong>Results: </strong>The Xception-based model, FluDeep-XR, achieved a mean square error of 0.738 in the external validation dataset. The Random Forest-based late fusion model, FluDeep, outperformed all the other models, achieving an area under the receiver operating curve of 0.818 and a sensitivity of 0.706 in the external dataset. Activation maps highlighted clear lung fields. Shapley additive explanations identified age, C-reactive protein, hematocrit, heart rate, and respiratory rate as the top 5 important clinical features.</p><p><strong>Discussion: </strong>The integration of medical imaging with objective clinical data outperformed single-modality models to predict 30-day mortality in influenza patients. We ensured the explainability of our models aligned with clinical knowledge and validated its applicability across foreign institutions.</p><p><strong>Conclusion: </strong>FluDeep highlights the potential of combining radiological and clinical information in late fusion design, enhancing diagnostic accuracy and offering an explainable, and generalizable decision support system.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689371","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":"Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing.","authors":"Jihye Kim Scroggins, Ismael I Hulchafo, Sarah Harkins, Danielle Scharp, Hans Moen, Anahita Davoudi, Kenrick Cato, Michele Tadiello, Maxim Topaz, Veronica Barcelona","doi":"10.1093/jamia/ocae290","DOIUrl":"https://doi.org/10.1093/jamia/ocae290","url":null,"abstract":"<p><strong>Objective: </strong>To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).</p><p><strong>Materials and methods: </strong>We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset. Annotators labeled for exemplars of 5 stigmatizing and 1 positive/preferred language categories. We used a semantic similarity-based search approach to expand the initial dataset by adding additional exemplars, composing an enhanced dataset. We employed traditional classifiers (Support Vector Machine, Decision Trees, and Random Forest) and a transformer-based model, ClinicalBERT (Bidirectional Encoder Representations from Transformers) and BERT base. Models were trained and validated on initial and enhanced datasets and were tested on enhanced testing dataset.</p><p><strong>Results: </strong>In the initial dataset, we annotated 963 exemplars as stigmatizing or positive/preferred. The most frequently identified category was marginalized language/identities (n = 397, 41%), and the least frequent was questioning patient credibility (n = 51, 5%). After employing a semantic similarity-based search approach, 502 additional exemplars were added, increasing the number of low-frequency categories. All NLP models also showed improved performance, with Decision Trees demonstrating the greatest improvement (21%). ClinicalBERT outperformed other models, with the highest average F1-score of 0.78.</p><p><strong>Discussion: </strong>Clinical BERT seems to most effectively capture the nuanced and context-dependent stigmatizing language found in obstetric clinical notes, demonstrating its potential clinical applications for real-time monitoring and alerts to prevent usages of stigmatizing language use and reduce healthcare bias. Future research should explore stigmatizing language in diverse geographic locations and clinical settings to further contribute to high-quality and equitable perinatal care.</p><p><strong>Conclusion: </strong>ClinicalBERT effectively captures the nuanced stigmatizing language in obstetric clinical notes. Our semantic similarity-based search approach to rapidly extract additional exemplars enhanced the performances while reducing the need for labor-intensive annotation.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683210","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":"Distributed, immutable, and transparent biomedical limited data set request management on multi-capacity network.","authors":"Yufei Yu, Maxim Edelson, Anh Pham, Jonathan E Pekar, Brian Johnson, Kai Post, Tsung-Ting Kuo","doi":"10.1093/jamia/ocae288","DOIUrl":"https://doi.org/10.1093/jamia/ocae288","url":null,"abstract":"<p><strong>Objective: </strong>Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.</p><p><strong>Materials and methods: </strong>We developed a blockchain-based system with six types of smart contracts to automate the LDS sharing process among major stakeholders. Our workflow included metadata initialization, access-request processing, and audit-log querying. We evaluated our system using synthetic data on three machines with varying specifications to emulate real-world scenarios. The data employed included ∼1000 researcher requests and ∼360 000 log queries.</p><p><strong>Results: </strong>On average, it took ∼2.5 s to register and respond to a researcher access request. The average runtime for an audit-log query with non-empty output was ∼3 ms. The runtime metrics at each institution showed general trends affiliated with their computational capacity.</p><p><strong>Discussion: </strong>Our system can reduce the LDS sharing request time from potentially hours to seconds, while enhancing data access transparency in a multi-institutional setting. There were variations in performance across sites that could be attributed to differences in hardware specifications. The performance gains became marginal beyond certain hardware thresholds, pointing to the influence of external factors such as network speeds.</p><p><strong>Conclusion: </strong>Our blockchain-based system can potentially accelerate clinical research by strengthening the data access process, expediting access and delivery of data links, increasing transparency with clear audit trails, and reinforcing trust in medical data management. Our smart contracts are available at: https://github.com/graceyufei/LDS-Request-Management.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683182","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":"Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.","authors":"Laura G Militello, Julie Diiulio, Debbie L Wilson, Khoa A Nguyen, Christopher A Harle, Walid Gellad, Wei-Hsuan Lo-Ciganic","doi":"10.1093/jamia/ocae291","DOIUrl":"https://doi.org/10.1093/jamia/ocae291","url":null,"abstract":"<p><strong>Objectives: </strong>To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).</p><p><strong>Materials and methods: </strong>This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.</p><p><strong>Results: </strong>The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development.</p><p><strong>Discussion: </strong>Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications.</p><p><strong>Conclusion: </strong>This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683165","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}