AI Data

Articles published in this theme: 19 (scroll down to load remaining articles)

2025

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AI’s Accuracy in Extracting Learning Experiences From Clinical Practice Logs: Observational Study

Takeshi Kondo, Hiroshi Nishigori

JMIR Med Educ 2025 (Oct 15); 11:e68697

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Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers for International Statistical Classification of Diseases, Tenth Revision, Code Prediction in Norwegian Clinical Texts: Model Development and Evaluation Study

Phuong Dinh Ngo, Miguel Ángel Tejedor Hernández, Taridzo Chomutare, Andrius Budrionis, Therese Olsen Svenning, Torbjørn Torsvik, Anastasios Lamproudis, Hercules Dalianis

JMIR AI 2025 (Aug 25); 4:e66153

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Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study

Lisa Pilgram, Samer El Kababji, Dan Liu, Khaled El Emam

J Med Internet Res 2025 (Aug 18); 27:e77893

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Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures

Richard Gebler, Ines Reinecke, Martin Sedlmayr, Miriam Goldammer

J Med Internet Res 2025 (Aug 01); 27:e74976

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Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability

Amelie Gyrard, Somayeh Abedian, Philip Gribbon, George Manias, Rick van Nuland, Kurt Zatloukal, Irina Emilia Nicolae, Gabriel Danciu, Septimiu Nechifor, Luis Marti-Bonmati, Pedro Mallol, Stefano Dalmiani, Serge Autexier, Mario Jendrossek, Ioannis Avramidis, Eva Garcia Alvarez, Petr Holub, Ignacio Blanquer, Anna Boden, Rada Hussein

J Med Internet Res 2025 (Mar 24); 27:e66273

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Utility-based Analysis of Statistical Approaches and Deep Learning Models for Synthetic Data Generation With Focus on Correlation Structures: Algorithm Development and Validation

Marko Miletic, Murat Sariyar

JMIR AI 2025 (Mar 20); 4:e65729

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2024

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Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods

Arnold Kamis, Nidhi Gadia, Zilin Luo, Shu Xin Ng, Mansi Thumbar

JMIR AI 2024 (Aug 29); 3:e58455

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Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation

Kyeryoung Lee, Zongzhi Liu, Yun Mai, Tomi Jun, Meng Ma, Tongyu Wang, Lei Ai, Ediz Calay, William Oh, Gustavo Stolovitzky, Eric Schadt, Xiaoyan Wang

JMIR AI 2024 (Jul 29); 3:e50800

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Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study

Annalisa Baronetto, Luisa Graf, Sarah Fischer, Markus F Neurath, Oliver Amft

JMIR AI 2024 (Jul 10); 3:e51118

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Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling

Tahsin Mullick, Sam Shaaban, Ana Radovic, Afsaneh Doryab

JMIR AI 2024 (May 20); 3:e47805

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Evaluating the Diagnostic Performance of Symptom Checkers: Clinical Vignette Study

Mohammad Hammoud, Shahd Douglas, Mohamad Darmach, Sara Alawneh, Swapnendu Sanyal, Youssef Kanbour

JMIR AI 2024 (Apr 29); 3:e46875

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Generating Synthetic Electronic Health Record Data Using Generative Adversarial Networks: Tutorial

Chao Yan, Ziqi Zhang, Steve Nyemba, Zhuohang Li

JMIR AI 2024 (Apr 22); 3:e52615

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Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Julian Späth, Zeno Sewald, Niklas Probul, Magali Berland, Mathieu Almeida, Nicolas Pons, Emmanuelle Le Chatelier, Pere Ginès, Cristina Solé, Adrià Juanola, Josch Pauling, Jan Baumbach

JMIR AI 2024 (Mar 29); 3:e47652

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The Impact of Expectation Management and Model Transparency on Radiologists’ Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

Lotte J S Ewals, Lynn J J Heesterbeek, Bin Yu, Kasper van der Wulp, Dimitrios Mavroeidis, Mathias Funk, Chris C P Snijders, Igor Jacobs, Joost Nederend, Jon R Pluyter, e/MTIC Oncology group

JMIR AI 2024 (Mar 13); 3:e52211

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Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study

Felix Brann, Nicholas William Sterling, Stephanie O Frisch, Justin D Schrager

JMIR AI 2024 (Jan 25); 3:e49784

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Learning From International Comparators of National Medical Imaging Initiatives for AI Development: Multiphase Qualitative Study

Kassandra Karpathakis, Emma Pencheon, Dominic Cushnan

JMIR AI 2024 (Jan 04); 3:e51168

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2023

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Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study

Samir Kendale, Andrew Bishara, Michael Burns, Stuart Solomon, Matthew Corriere, Michael Mathis

JMIR AI 2023 (Sep 08); 2:e44909

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Artificial Intelligence–Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study

Alfred Sorbello, Syed Arefinul Haque, Rashedul Hasan, Richard Jermyn, Ahmad Hussein, Alex Vega, Krzysztof Zembrzuski, Anna Ripple, Mitra Ahadpour

JMIR AI 2023 (Jul 18); 2:e45000

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A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

Hossein Naseri, Sonia Skamene, Marwan Tolba, Mame Daro Faye, Paul Ramia, Julia Khriguian, Marc David, John Kildea

JMIR AI 2023 (May 22); 2:e44779

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