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npj Digital Medicine is a peer-reviewed journal published by Springer Nature, covering Artificial Intelligence in Healthcare and Education, Machine Learning in Healthcare, Digital Mental Health Interventions. Over its lifetime it has published 2,898 papers, which have received 120,688 citations.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. npj Digital Medicine 12, 45–58 (2023).
Formats any DOI in npj Digital Medicine style. No sign-up. |
| Publishes research in | Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare Digital Mental Health Interventions Mobile Health and mHealth Applications AI in cancer detection |
| ISSN | 2398-6352 |
| Citation impact (2-yr) | 11.68 |
| h-index | 161 |
| i10-index | 1,386 |
| Article processing charge | $3,060 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Harvard University |
| Journal website | www.nature.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been…
Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However,…
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw…
Artificial Intelligence (AI) has long promised to increase healthcare affordability, quality and accessibility but FDA, until recently, had never authorized an autonomous AI diagnostic system. This pivotal trial of an AI system to detect diabetic retinopathy (DR) in people with diabetes enrolled 900 subjects, with no history of DR at primary care clinics, by comparing…
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment.…
