Journal of the American Medical Informatics Association最新文献

{"title":"Tipping the balance: impact of class imbalance correction on the performance of clinical risk prediction models.","authors":"Amalie Koch Andersen, Hadi Mehdizavareh, Arijit Khan, Tobias Becher, Simone Britsch, Markward Britsch, Morten Bøttcher, Simon Winther, Palle Duun Rohde, Morten Hasselstrøm Jensen, Simon Lebech Cichosz","doi":"10.1093/jamia/ocag127","DOIUrl":"10.1093/jamia/ocag127","url":null,"abstract":"<p><strong>Objectives: </strong>Machine-learning-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied to address rare outcomes, their impact on probabilistic calibration remains insufficiently understood. This study evaluated the effect of widely used resampling strategies on both discrimination and calibration across real-world clinical prediction tasks.</p><p><strong>Materials and methods: </strong>Ten clinical datasets spanning diverse medical domains and including over 600 000 patients were analyzed. Multiple machine-learning model families were evaluated. Models were trained on original data and using 3 1:1 class-imbalance correction strategies (synthetic minority oversampling technique, random undersampling, and random oversampling). Performance was assessed on held-out data using discrimination and calibration metrics.</p><p><strong>Results: </strong>Resampling had no positive impact on predictive performance. Changes in area under the receiver operating characteristic curve (ROC-AUC) and precision-recall AUC were small and inconsistent (ROC-AUC: -0.002 to -0.01; PR-AUC: -0.10 to -0.03), with no method showing systematic improvement. In contrast, calibration was consistently degraded. Resampled models showed higher Brier scores (increase 0.029-0.080) and marked deviations in calibration intercept and slope, indicating distorted predicted risks despite preserved ranking performance.</p><p><strong>Discussion: </strong>Across diverse clinical datasets, resampling primarily altered the implicit class prior learned during training, leading to miscalibration when models were evaluated. The consistent dissociation between discrimination and calibration highlights that rank-based metrics alone are insufficient for evaluating clinical utility. Gains from imbalance correction can typically be reproduced by threshold adjustment without distorting predicted probabilities.</p><p><strong>Conclusion: </strong>Common 1:1 class-imbalance correction techniques do not improve discrimination and may substantially degrade calibration, limiting their suitability for clinical risk prediction where accurate probabilities are essential.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1943-1956"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632435","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":"Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.","authors":"Simone Castagno, Alagu Subramanian, Ilias E Epanomeritakis, Benjamin Gompels, Stephen McDonnell, Mark Birch, Mihaela van der Schaar, Andrew McCaskie","doi":"10.1093/jamia/ocag102","DOIUrl":"10.1093/jamia/ocag102","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the clinical applications and translation readiness of model-based synthetic tabular data in healthcare, and identify gaps in governance reporting that may hinder translation.</p><p><strong>Materials and methods: </strong>We systematically searched Ovid MEDLINE and Embase (2010-August 2025; PROSPERO: CRD42025635514) for studies that generated and applied model-based synthetic tabular data in clinical contexts. Screening used a \"human-in-the-loop\" large language model workflow alongside independent manual review, achieving 100% sensitivity for included studies. Unlike prior reviews focused primarily on evaluation methodology, we mapped use-cases and deployment paradigms, and audited translation-readiness reporting using a predefined governance framework (validation depth, privacy, fairness, regulatory alignment).</p><p><strong>Results: </strong>Thirty-seven studies (2019-2025) were included. GANs predominated; other approaches included VAEs, diffusion models, LLM-based synthesis, and Bayesian networks. Dataset augmentation was the primary application, often improving downstream model performance for rare outcomes. Emerging applications included synthetic control cohorts and algorithmic bias mitigation. Translation-readiness reporting was limited: 34/37 studies (92%) relied solely on internal validation, 9/37 (24%) used formal privacy models, 6/37 (16%) reported explicit fairness evaluations, and 6/37 (16%) addressed regulatory alignment. Few studies distinguished \"no-release\" from \"delayed-release\" paradigms.</p><p><strong>Discussion: </strong>A systemic gap exists between methodological innovation and deployment-readiness reporting. Model-based synthetic data show clear value for augmentation and class balancing, but inconsistent reporting of validation, privacy, fairness, and regulatory considerations limits confidence in clinical deployment.</p><p><strong>Conclusion: </strong>We propose TRUST-SD (Transparency and Reporting for Utility, Safety, and Translation of Synthetic Data), an author-derived, preliminary, evidence-informed reporting checklist spanning 7 domains, as a starting point for community refinement and consensus-building.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2008-2028"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279702","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":"The importance of clinical context in evaluating algorithmic fairness: insights from a medication adherence prediction algorithm.","authors":"Amrita Mukhopadhyay, Yunan Zhao, Rumi Chunara, Ian M Kronish, Steven Lawrence, Saul Blecker, Samrachana Adhikari","doi":"10.1093/jamia/ocag097","DOIUrl":"10.1093/jamia/ocag097","url":null,"abstract":"<p><strong>Objective: </strong>Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases.</p><p><strong>Materials and methods: </strong>This was a retrospective study of patients with heart failure (HF) at an academic health system using an algorithm that predicts pharmacy fill nonadherence to evidence-based HF medications. We compared prediction performance metrics (accuracy, false positive rate, false negative rate), using rate-ratios (RRs), between subgroups with and without known HF care disparities: below vs above median neighborhood-level socioeconomic status (nSES) and Black vs White race. Results were then applied to 3 hypothetical clinical use cases.</p><p><strong>Results: </strong>Among 34 697 patients (13% Black, 10% Hispanic, 65% White), algorithm accuracy was similar across nSES and racial subgroups. The algorithm assigned more false positives for medication nonadherence among low vs high nSES (RR [95%CI] 1.50 [1.44-1.56]) and Black vs White (2.05 [1.92-2.19]) subgroups. The algorithm also assigned fewer false negatives (0.63 [0.59-0.67]) to Black vs White subgroups. When applied to 3 hypothetical use cases, worsening of existing disparities was pertinent for clinical applications where false positives could be particularly harmful (e.g, if predictions of nonadherence prompted lower treatment priority).</p><p><strong>Discussion: </strong>Although accuracy was similar across demographic groups, differences in false positive and false negative rates revealed that the same prediction may worsen disparities in some use cases, but not others.</p><p><strong>Conclusion: </strong>Evaluation of predictions in the context of clinical use is essential to avoid unintentionally worsening inequities.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1866-1874"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148334098","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":"Guideline-grounded large language models for extracting genome-informed clinical recommendations from electronic health records.","authors":"Yi Xin, Kyle W Davis, Wu-Chen Su, Chao Yan, Tyler Derr, Kavishwar B Wagholikar, Devi Priyanka Maripuri, Chunhua Weng, Casey Ta, Iftikhar J Kullo, Krzysztof Kiryluk, Elisabeth A Rosenthal, James J Cimino, Shawn Murphy, Wei-Qi Wei","doi":"10.1093/jamia/ocag171","DOIUrl":"https://doi.org/10.1093/jamia/ocag171","url":null,"abstract":"<p><strong>Background: </strong>Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automated extraction, but their performance in genome-informed clinical contexts remains incompletely characterized.</p><p><strong>Objectives: </strong>To evaluate guideline-grounded LLM approaches for extracting genome-informed clinical recommendations from EHR notes in the eMERGE study.</p><p><strong>Materials and methods: </strong>We developed an LLM-based pipeline to identify clinical recommendations associated with GIRA reports. Three LLMs (GPT-4o, LLaMA-3.3-70B, and LLaMA-3.1-8B) were evaluated using baseline prompting, guideline-aware prompting, and retrieval-augmented generation (RAG), validated against manual chart review (N = 18 participants; N = 34 documents).</p><p><strong>Results: </strong>Baseline prompting showed limited performance (F1 = 0.50-0.54). Incorporating guideline knowledge improved F1 by 0.14-0.31 across models, with LLaMA-3.1-8B using RAG achieving the highest performance (F1 = 0.81).</p><p><strong>Conclusion: </strong>Domain-grounded LLM approaches can support scalable extraction of genome-informed clinical recommendations from EHR data. Larger studies are needed to assess generalizability in real-world workflows.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117773","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":"Benchmarking information extraction of physical activity from electronic health record with large language models: an natural language processing pipeline and comparative evaluation.","authors":"Han Yang, Zhongran Niu, Mingchen Li, Huixue Zhou, Yongkang Xiao, Sicheng Zhou, Zaifu Zhan, Ying Liu, Shiqin Liu, Christopher J Tignanelli, Genevieve B Melton, Rui Zhang","doi":"10.1093/jamia/ocag101","DOIUrl":"10.1093/jamia/ocag101","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to develop a data model and a natural language processing (NLP) pipeline for representing physical activity (PA) in Electronic Health Records (EHRs), and to evaluate transformer- and Large Language Model (LLM)-based classifiers for sentence-level PA attribute classification.</p><p><strong>Materials and methods: </strong>We analyzed PA documentation across three patient cohorts (cancer, COVID, and Alzheimer's disease) using structured and unstructured EHR data. A conceptual schema was developed to represent PA and its linguistic attributes. Five BERT models and three modern LLMs (Llama3-8B, MedAlpaca-13B, and PMC-Llama-13B) were evaluated for classifying PA attributes (binary status, negation, exclusion, and an eleven-class Category) on pre-extracted PA-related sentences.</p><p><strong>Results: </strong>Clinical notes were a richer source of PA information than structured ICD or SDoH data. On binary tasks, the best BERT model reached F1 0.619 (Exclusion); with Supervised Fine-Tuning (SFT), Llama3-8B reached F1 0.689 (Exclusion). On the 11-class Category task, performance was modest (best macro-F1 0.262, ROC-AUC 0.803, by Llama3-8B).</p><p><strong>Discussion: </strong>In-Context Learning (ICL) was highly variable: while Llama3-8B-ICL achieved the best ROC-AUC on Category, the domain-specific MedAlpaca-13B and PMC-LLaMA-13B essentially failed. These results, together with sparsely represented PA elements (Amount, Frequency, Assessment) and the absence of a downstream evaluation, position this work as an initial proof of feasibility, with supervised domain adaptation still required for reliable clinical PA extraction.</p><p><strong>Conclusion: </strong>We contribute a PA data model, annotation schema, and a working NLP pipeline with a BERT/LLM benchmark for sentence-level PA attribute classification. The pipeline supports future end-to-end PA extraction and downstream applications such as phenotyping, risk prediction, and cohort identification.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1875-1887"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296977","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":"SMART: structured, meaningful, auditable, responsible, and transparent documentation for clinical AI.","authors":"Ankur Lohachab, Frédéric Jung, Stef Rommes, Gökhan Ertaylan, Anshu Ankolekar, Visara Urovi","doi":"10.1093/jamia/ocag117","DOIUrl":"10.1093/jamia/ocag117","url":null,"abstract":"<p><strong>Objective: </strong>Clinical AI systems and models increasingly need traceable documentation that makes intended use, performance evidence, transparency, and lifecycle status explicit, yet existing model cards, datasheets, reporting guidelines, and other documentation approaches do not consistently bring together structured clinical fields, vocabulary-linked cohort descriptions, structured performance reporting, and governed documentation history. We introduce Structured, Meaningful, Auditable, Responsible, and Transparent (SMART), a framework that adapts the model card concept into structured, lifecycle-aware documentation with OMOP integration, role-based lifecycle governance, and a blockchain-backed audit trail for verifying documentation integrity and lifecycle history.</p><p><strong>Materials and methods: </strong>Following a design-science-informed approach, we analyzed documentation requirements, reviewed existing approaches, implemented SMART through open-source packages and smart contracts, and evaluated it using example SMART model cards, an illustrative COPD exacerbation risk-prediction documentation case study, and blockchain-governed lifecycle experiments.</p><p><strong>Results: </strong>The design process resulted in the SMART framework in which documentation provisions are reflected across Schema, Lifecycle, and Chain (ie, Blockchain) components. Evaluation showed that, in multi-party settings, documentation history remains independently verifiable because lifecycle actions are role-attributed and linked to content hashes; median testnet gas use ranged from 352 722 to 852 399 gas across the evaluated lifecycle operations.</p><p><strong>Discussion: </strong>SMART positions clinical AI documentation as a shared infrastructure for preliminary model assessment, making role attribution, lifecycle state, and documentation changes more transparent and traceable over time.</p><p><strong>Conclusion: </strong>By providing an implementable framework, SMART illustrates how clinical AI documentation can be structured, clinically contextualized, auditable, responsible, transparent, and lifecycle-aware.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1921-1933"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800881","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":"Institutional data commons: a federated Data Use Certification-aware architecture for secure and scalable data use in biomedical data ecosystems.","authors":"Saonli Basu","doi":"10.1093/jamia/ocag147","DOIUrl":"10.1093/jamia/ocag147","url":null,"abstract":"<p><strong>Background: </strong>Modern biomedical data ecosystems increasingly rely on global cloud platforms to coordinate access to large-scale genomic and clinical datasets. However, operational governance remains largely investigator-centric, shifting the responsibility for complex security, compliance, and infrastructure management to individual laboratories. As data volumes and regulatory requirements expand, this approach fails to scale across the research enterprise. This disjointed approach creates a substantial governance burden and can slow down scientific progress. In centralized cloud environments, investigators face siloed identity management and high costs, leading to inefficient data use and increased risk when integrating local and global datasets.</p><p><strong>Materials and methods: </strong>We examine limitations in the current infrastructure and propose reframing institutional data commons as governance-aware intermediaries to ensure secure, efficient and sustainable use of controlled-access biomedical data.</p><p><strong>Results: </strong>This federated architecture decouples storage from authorization, enabling dynamic access linked to active certifications, whether data are analyzed in situ on global platforms or in local governance-aware institutional access environments.</p><p><strong>Discussion: </strong>Shifting governance from investigators to institutional infrastructure ensures that biomedical research remains both secure and economically sustainable.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2070-2075"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992316","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":"Association of medical discrimination and hypertension in adults in the All of Us Research Program.","authors":"Vincent Pepe, Yashika Sharma, Meghan Reading Turchioe, Ian M Kronish, Yu Zheng Lim, Billy A Caceres","doi":"10.1093/jamia/ocag126","DOIUrl":"https://doi.org/10.1093/jamia/ocag126","url":null,"abstract":"<p><strong>Objectives: </strong>To examine the associations of medical discrimination with hypertension (HTN) outcomes.</p><p><strong>Materials and methods: </strong>Using cross-sectional data from the All of Us Research Program (2018-2022), we defined elevated blood pressure (BP) as systolic BP ≥130 or diastolic BP ≥ 80 mm Hg. We defined HTN diagnosis as either (1) self-reported HTN or (2) a HTN billing code. Among participants with HTN, we assessed antihypertensive medication use and HTN control. Logistic regression models estimated the associations of medical discrimination (using the Discrimination in Medical Settings Scale) with each BP outcome. The full sample included 29 805 adults, of whom 10 359 adults had elevated BP.</p><p><strong>Results: </strong>Medical discrimination was associated with higher odds of elevated BP (adjusted odds ratio [AOR] 1.05, 95% confidence interval [CI], 1.02-1.08) and HTN diagnosis (AOR 1.06, 95% CI, 1.02-1.10). Among participants with HTN, medical discrimination was not associated with treatment or control.</p><p><strong>Discussion: </strong>Findings suggest that medical discrimination contributes to increased prevalence of elevated BP and HTN diagnosis.</p><p><strong>Conclusion: </strong>Researchers should examine mechanisms linking medical discrimination with HTN.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117701","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":"Small language models in clinical medicine: a systematic review of performance, safety, and deployment feasibility.","authors":"Alon Gorenshtein, Mahmud Omar, Yiftach Barash, Jonathan B Kruskal, Muneeb Ahmed, Olga R Brook, Ben Illigens, Girish N Nadkarni, Eyal Klang","doi":"10.1093/jamia/ocag164","DOIUrl":"https://doi.org/10.1093/jamia/ocag164","url":null,"abstract":"<p><strong>Objectives: </strong>To review the clinical evidence for small language models (SLMs), 4 billion parameters or fewer, for performance, safety, and deployment feasibility.</p><p><strong>Materials and methods: </strong>We searched 5 databases through February 10, 2026 for English-language reports of an SLM on a clinical task, following PRISMA 2020 under a PROSPERO-registered protocol. Two reviewers independently assessed eligibility (Cohen κ = 0.93) and rated methodological quality and transparency. We calculated a relative task score (RTS): the primary metric of each study's best-performing qualifying SLM, divided by a study-specific reference comparator selected by a uniform hierarchy, calculated by the review authors.</p><p><strong>Results: </strong>Eleven studies (7 peer-reviewed, 4 preprint or technical-report) were eligible. Across the 9 studies with a ratio-scale reference comparator, RTS ranged from 0.30 to 2.36; 1 was a raw difference on a non-ratio scale and 1 had no comparator. We did not pool estimates, given heterogeneity. Hallucination was evaluated in 6 of 11 studies, a scalar calibration-error metric or epistemic uncertainty in 0 of 11 (1 reported a calibration curve only), and on-hardware inference timing in 2 of 11. Our memory model estimated that a 4-billion-parameter model requires about 9.3 GB at a 2048-token context, indicating single-GPU memory feasibility rather than demonstrated deployment.</p><p><strong>Discussion: </strong>Domain-adapted SLMs are memory-feasible, but the evidence base is too small and heterogeneous for inferential comparison with larger models, and safety properties (including adversarial robustness) go unmeasured.</p><p><strong>Conclusion: </strong>Routine reporting of calibration and uncertainty and measurement of end-to-end clinical performance are prerequisites for responsible SLM deployment.</p><p><strong>Prospero registration: </strong>CRD420261331444.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117806","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":"Comments on extensions to the fundamental theorem of Biomedical Informatics for the Artificial Intelligence era.","authors":"Vimla L Patel, Edward H Shortliffe","doi":"10.1093/jamia/ocag170","DOIUrl":"https://doi.org/10.1093/jamia/ocag170","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117801","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}