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Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review
JMIR AI 2023;2(1):e42936
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| 1398 | 4 | 16 | |
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Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation
JMIR AI 2023;2(1):e40167
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| 721 | 9 | 13 | |
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Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study
JMIR AI 2023;2(1):e44358
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| 1909 | 11 | 13 | |
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The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic: Scoping Review
JMIR AI 2023;2(1):e38397
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| 1001 | 1 | 12 | |
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Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation
JMIR AI 2023;2(1):e41205
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| 362 | 11 | 10 | |
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Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial
JMIR AI 2023;2(1):e49023
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| 237 | 9 | 10 | |
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Preparing for an Artificial Intelligence–Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings
JMIR AI 2023;2(1):e40973
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| 927 | 8 | 10 | |
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Role of Ethics in Developing AI-Based Applications in Medicine: Insights From Expert Interviews and Discussion of Implications
JMIR AI 2024;3(1):e51204
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| 373 | 4 | 10 | |
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Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach
JMIR AI 2023;2(1):e52888
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| 845 | 15 | 9 | |
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Adolescents’ Well-being While Using a Mobile Artificial Intelligence–Powered Acceptance Commitment Therapy Tool: Evidence From a Longitudinal Study
JMIR AI 2022;1(1):e38171
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| 1330 | 4 | 9 | |
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Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?
JMIR AI 2023;2(1):e42940
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| 509 | 34 | 8 | |
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Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review
JMIR AI 2023;2(1):e46717
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| 396 | 5 | 8 | |
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Chronic Disease Prediction Using the Common Data Model: Development Study
JMIR AI 2022;1(1):e41030
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| 444 | 2 | 8 | |
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Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study
JMIR AI 2023;2(1):e47353
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| 627 | 6 | 7 | |
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A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study
JMIR AI 2024;3(1):e52171
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| 319 | 2 | 7 | |
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Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study
JMIR AI 2022;1(1):e41940
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| 172 | 4 | 6 | |
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Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study
JMIR AI 2023;2(1):e44909
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| 317 | 3 | 6 | |
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Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study
JMIR AI 2023;2(1):e46769
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| 451 | 3 | 6 | |
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Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study
JMIR AI 2023;2(1):e44293
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| 350 | 4 | 5 | |
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Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis
JMIR AI 2023;2(1):e47283
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| 3397 | 81 | 5 | |
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Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study
JMIR AI 2024;3(1):e51834
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| 283 | 3 | 5 | |
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Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study
JMIR AI 2023;2(1):e40843
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| 376 | 10 | 5 | |
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Artificial Intelligence in Health Care—Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study
JMIR AI 2023;2(1):e46487
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| 557 | 3 | 5 | |
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Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study
JMIR AI 2024;3(1):e52095
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| 384 | 11 | 5 | |
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Toward Clinical Generative AI: Conceptual Framework
JMIR AI 2024;3(1):e55957
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| 652 | 7 | 4 | |