OOIR: Observatory of International Research

Papers

(The H4-Index of npj Digital Medicine is 65. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)

Article	Citations
The future of digital health with federated learning	896
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database	520
Deep learning-enabled medical computer vision	463
Machine learning-based prediction of COVID-19 diagnosis based on symptoms	315
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis	291
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines	261
Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction	245
Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)	230
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare	216
Digital inclusion as a social determinant of health	202
Machine learning for medical imaging: methodological failures and recommendations for the future	188
A short guide for medical professionals in the era of artificial intelligence	177
Digitizing clinical trials	176
Chatbots in the fight against the COVID-19 pandemic	175
Do as AI say: susceptibility in deployment of clinical decision-aids	163
Challenges for the evaluation of digital health solutions—A call for innovative evidence generation approaches	159
Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies	158
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review	146
The emerging clinical role of wearables: factors for successful implementation in healthcare	143
Second opinion needed: communicating uncertainty in medical machine learning	143
Assessment of physiological signs associated with COVID-19 measured using wearable devices	141
A large language model for electronic health records	141
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study	136
Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers	133
Automated abnormality classification of chest radiographs using deep convolutional neural networks	129

Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research	127
Patient apprehensions about the use of artificial intelligence in healthcare	120
U-Sleep: resilient high-frequency sleep staging	120
The imperative for regulatory oversight of large language models (or generative AI) in healthcare	118
The need for a system view to regulate artificial intelligence/machine learning-based software as medical device	116
Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery	115
A framework for digital health equity	112
International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium	110
Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries	107
Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities	106
Digital medicine and the curse of dimensionality	105
What do medical students actually need to know about artificial intelligence?	98
Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19	95
Interpretable survival prediction for colorectal cancer using deep learning	93
Developing a delivery science for artificial intelligence in healthcare	93
Multimodal machine learning in precision health: A scoping review	90
Natural language processing applied to mental illness detection: a narrative review	90
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software	89
Deep representation learning of electronic health records to unlock patient stratification at scale	88
The potential of artificial intelligence to improve patient safety: a scoping review	87
Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic	86
Actionable health app evaluation: translating expert frameworks into objective metrics	84
Tracking COVID-19 using online search	83
A systematic review of smartphone-based human activity recognition methods for health research	82
Characteristics and challenges of the clinical pipeline of digital therapeutics	82
A machine learning approach predicts future risk to suicidal ideation from social media data	82
Predicting COVID-19 mortality with electronic medical records	81
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare	81
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department	81
Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation	80
Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use	79
Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement	76
Preventive digital mental health interventions for children and young people: a review of the design and reporting of research	74
Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?	71
Translating evidence into practice: eligibility criteria fail to eliminate clinically significant differences between real-world and study populations	70
Digital health tools for the passive monitoring of depression: a systematic review of methods	70
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV	66
Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate	66
CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images	66
Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors	66
The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field	65
Privacy protections to encourage use of health-relevant digital data in a learning health system	65