OOIR: Observatory of International Research

Papers

(The H4-Index of Lancet Digital Health 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 2022-05-01 to 2026-05-01.)

Article	Citations
Retraction remedy: a resource for transparent science	631
Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models	470
Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis	456
Correction to Lancet Digit Health 2023; 5: e446–57	426
Correction to Lancet Digit Health 2024; 6: e791–802	396
Machine learning to predict type 1 diabetes in children	385
ChatGPT: the future of discharge summaries?	350
Generative Pre-trained Transformer 4 (GPT-4) in clinical settings	288
Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world	248
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study	236
In the era of digitalisation and biosignatures, is C-reactive protein still the one to beat?	231
Targeting respiratory syncytial virus vaccination using individual prediction	223
Challenges for augmenting intelligence in cardiac imaging	220
Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies	212
A deep-learning-enabled diagnosis of ovarian cancer – Authors' reply	202
Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study	201
Accelerating action for gender equality in health	198
Balancing AI innovation with patient safety	197
Technology for world elimination of neglected tropical diseases	195
Health insights from face photographs	155
Harnessing population-wide health data to predict cancer risk	153
Fairly evaluating the performance of normative models	150
A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning stud	149
Digital health funding for COVID-19 vaccine deployment across four major donor agencies	143
Large language model integration in Philippine ophthalmology: early challenges and steps forward	141

AI-CAD for tuberculosis and other global high-burden diseases	137
Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study	133
Drone delivery of automated external defibrillators compared with ambulance arrival in real-life suspected out-of-hospital cardiac arrests: a prospective observational study in Sweden	126
Bridging the gap: aligning clinical decision support regulation with clinical practice in the era of artificial intelligence	123
A future role for health applications of large language models depends on regulators enforcing safety standards	118
Efficacy of standalone smartphone apps for mental health: an updated systematic review and meta-analysis	113
Trust, not technology: governing access to health data as the decisive challenge for the UK	112
Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study	112
Effect of wearable activity trackers on physical activity in children and adolescents: a systematic review and meta-analysis	112
Decolonising health data	106
Synthetic data, synthetic trust: navigating data challenges in the digital revolution	104
Using artificial intelligence to switch from accident to sagacity in the serendipitous detection of uncommon diseases	102
Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study	99
Effect of digital psychoeducation and peer support on the mental health of family carers supporting individuals with psychosis in England (COPe-support): a randomised clinical trial	98
A long STANDING commitment to improving health care	96
Thank you to The Lancet Digital Health's statistical and peer reviewers in 2022	91
Can electronic medical records predict neonatal seizures?	91
Safe care from home for complicated pregnancies?	86
US COVID-19 clinical trial leadership gender disparities	85
The MAIDA initiative: establishing a framework for global medical-imaging data sharing	84
A practical framework for operationalising responsible and equitable artificial intelligence in health care: tackling bias, inequity, and implementation challenges	83
Radiomics in neuro-oncological clinical trials	83
An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation	83
Interpreting the GRACE 3.0 ITE model: from predictive performance to clinical decision utility	82
Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intel	80
Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study	78
Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study	76
Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study	73
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations	72
A response to evaluating national data flows	72
Comprehensive genomic profiling and treatment patterns across ancestries in advanced prostate cancer: a large-scale retrospective analysis	71
Overlooked and under-reported: the impact of cyberattacks on primary care in the UK National Health Service	70
Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study	69
Moving forward with machine learning models in acute chest pain	69
RareArena: a comprehensive benchmark dataset unveiling the potential of large language models in rare disease diagnosis	68
AI-enabled forecasting of prehospital transfusion needs in patients with trauma: a multinational, registry-based, retrospective, machine learning development and validation study	68
A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan	68
Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study	68
Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis	67
Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study	67
Automated retinal image analysis systems to triage for grading of diabetic retinopathy: a large-scale, open-label, national screening programme in England	65
Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study	65