| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study
|
Fung, Russell
|
|
|
|
7 |
p. e368-e375
|
article
|
| 2 |
A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study
|
Kwon, Joon-myoung
|
|
|
|
7 |
p. e358-e367
|
article
|
| 3 |
A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study
|
Azhie, Amirhossein
|
|
|
|
7 |
p. e458-e466
|
article
|
| 4 |
A deep-learning model to assist thyroid nodule diagnosis and management
|
Zhang, Bin
|
|
|
|
7 |
p. e410
|
article
|
| 5 |
A deep-learning model to assist thyroid nodule diagnosis and management
|
Lee, Joon-Hyop
|
|
|
|
7 |
p. e409
|
article
|
| 6 |
A deep-learning model to assist thyroid nodule diagnosis and management – Authors' reply
|
Liu, Yihao
|
|
|
|
7 |
p. e411-e412
|
article
|
| 7 |
A deep neural network trained to interpret results from electrocardiograms: better than physicians?
|
Sinnecker, Daniel
|
|
|
|
7 |
p. e332-e333
|
article
|
| 8 |
Africa's COVID-19 health technologies' watershed moment
|
Adepoju, Paul
|
|
|
|
7 |
p. e346-e347
|
article
|
| 9 |
An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: a national validation cohort study in England
|
Nafilyan, Vahé
|
|
|
|
7 |
p. e425-e433
|
article
|
| 10 |
An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study
|
Hiremath, Amogh
|
|
|
|
7 |
p. e445-e454
|
article
|
| 11 |
Artificial intelligence in health care: value for whom?
|
Zeitoun, Jean-David
|
|
|
|
7 |
p. e338-e339
|
article
|
| 12 |
Artificial intelligence to complement rather than replace radiologists in breast screening
|
Taylor-Phillips, Sian
|
|
|
|
7 |
p. e478-e479
|
article
|
| 13 |
Association between change in cardiovascular risk scores and future cardiovascular disease: analyses of data from the Whitehall II longitudinal, prospective cohort study
|
Lindbohm, Joni V
|
|
|
|
7 |
p. e434-e444
|
article
|
| 14 |
Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study
|
Zhu, Hongling
|
|
|
|
7 |
p. e348-e357
|
article
|
| 15 |
Big data and long COVID
|
The Lancet Digital Health,
|
|
|
|
7 |
p. e477
|
article
|
| 16 |
Clinical assessment as a part of an early warning score—a Danish cluster-randomised, multicentre study of an individual early warning score
|
Nielsen, Pernille B
|
|
|
|
7 |
p. e497-e506
|
article
|
| 17 |
Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis
|
Leibig, Christian
|
|
|
|
7 |
p. e507-e519
|
article
|
| 18 |
Correction to Lancet Digital Health 2019; 1: e271–97
|
|
|
|
|
7 |
p. e334
|
article
|
| 19 |
Correction to Lancet Digital Health 2019; 1: e261–70
|
|
|
|
|
7 |
p. e334
|
article
|
| 20 |
Correction to Lancet Digit Health 2021; 3: e250–59
|
|
|
|
|
7 |
p. e413
|
article
|
| 21 |
Correction to Lancet Digit Health 2021; 3: e360–70
|
|
|
|
|
7 |
p. e413
|
article
|
| 22 |
Correction to Lancet Digit Health 2021; 3: e317–29
|
|
|
|
|
7 |
p. e413
|
article
|
| 23 |
Correction to Lancet Digit Health 2023; 5: e404–20
|
|
|
|
|
7 |
p. e403
|
article
|
| 24 |
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
|
Thygesen, Johan H
|
|
|
|
7 |
p. e542-e557
|
article
|
| 25 |
Data sharing: keeping patients on board
|
Watts, Geoff
|
|
|
|
7 |
p. e332-e333
|
article
|
| 26 |
Deep learning-based radiomics: pacing immunotherapy in lung cancer
|
Sverzellati, Nicola
|
|
|
|
7 |
p. e396-e397
|
article
|
| 27 |
Deep learning models to detect hidden clinical correlates
|
Ouyang, David
|
|
|
|
7 |
p. e334-e335
|
article
|
| 28 |
Deep learning to stratify lung nodules on annual follow-up CT
|
Heuvelmans, Marjolein A
|
|
|
|
7 |
p. e324-e325
|
article
|
| 29 |
Deep learning using electrocardiographs
|
The Lancet Digital Health,
|
|
|
|
7 |
p. e331
|
article
|
| 30 |
Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study
|
Kwong, Jethro C C
|
|
|
|
7 |
p. e435-e445
|
article
|
| 31 |
Digital health equity for older populations
|
The Lancet Digital Health,
|
|
|
|
7 |
p. e395
|
article
|
| 32 |
Effectiveness of an mHealth system on access to eye health services in Kenya: a cluster-randomised controlled trial
|
Rono, Hillary
|
|
|
|
7 |
p. e414-e424
|
article
|
| 33 |
Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study
|
|
|
|
|
7 |
p. e520-e531
|
article
|
| 34 |
Fetal growth and gestational age prediction by machine learning
|
Ananth, Cande V
|
|
|
|
7 |
p. e336-e337
|
article
|
| 35 |
Genotyping SARS-CoV-2 through an interactive web application
|
Maan, Hassaan
|
|
|
|
7 |
p. e340-e341
|
article
|
| 36 |
Identifying adverse childhood experiences with electronic health records of linked mothers and children in England: a multistage development and validation study
|
Syed, Shabeer
|
|
|
|
7 |
p. e482-e496
|
article
|
| 37 |
Identifying who has long COVID in the USA: a machine learning approach using N3C data
|
Pfaff, Emily R
|
|
|
|
7 |
p. e532-e541
|
article
|
| 38 |
β-lactam microneedle array biosensors: a new technology on the horizon
|
Richter, Daniel C
|
|
|
|
7 |
p. e320-e321
|
article
|
| 39 |
Microneedle biosensors for real-time, minimally invasive drug monitoring of phenoxymethylpenicillin: a first-in-human evaluation in healthy volunteers
|
Rawson, Timothy M
|
|
|
|
7 |
p. e335-e343
|
article
|
| 40 |
Paradox of telemedicine: building or neglecting trust and equity
|
Yee, Vivian
|
|
|
|
7 |
p. e480-e481
|
article
|
| 41 |
Patient emergency health-care use before hospital admission for COVID-19 and long-term outcomes in Scotland: a national cohort study
|
Docherty, Annemarie B
|
|
|
|
7 |
p. e446-e457
|
article
|
| 42 |
Position statement on clinical evaluation of imaging AI
|
McCague, Cathal
|
|
|
|
7 |
p. e400-e402
|
article
|
| 43 |
Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study
|
Saad, Maliazurina B
|
|
|
|
7 |
p. e404-e420
|
article
|
| 44 |
Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
|
Huang, Peng
|
|
|
|
7 |
p. e353-e362
|
article
|
| 45 |
Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations
|
Canas, Liane S
|
|
|
|
7 |
p. e421-e434
|
article
|
| 46 |
Race representation matters in cancer care
|
The Lancet Digital Health,
|
|
|
|
7 |
p. e408
|
article
|
| 47 |
Reporting on deep learning algorithms in health care
|
Yu, Marco
|
|
|
|
7 |
p. e328-e329
|
article
|
| 48 |
Sharing patient-level real-time COVID-19 data
|
Komorowski, Matthieu
|
|
|
|
7 |
p. e345
|
article
|
| 49 |
Smartphone and social media-based cardiac rehabilitation and secondary prevention in China (SMART-CR/SP): a parallel-group, single-blind, randomised controlled trial
|
Dorje, Tashi
|
|
|
|
7 |
p. e363-e374
|
article
|
| 50 |
The effect of digital physical activity interventions on daily step count: a randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study
|
Shcherbina, Anna
|
|
|
|
7 |
p. e344-e352
|
article
|
| 51 |
The European artificial intelligence strategy: implications and challenges for digital health
|
Cohen, I Glenn
|
|
|
|
7 |
p. e376-e379
|
article
|
| 52 |
The evolving mHealth-based cardiac rehabilitation
|
Wang, Wenru
|
|
|
|
7 |
p. e326-e327
|
article
|
| 53 |
The hopes and hazards of using personal health technologies in the diagnosis and prognosis of infections
|
Radin, Jennifer M
|
|
|
|
7 |
p. e455-e461
|
article
|
| 54 |
The need for privacy with public digital contact tracing during the COVID-19 pandemic
|
Bengio, Yoshua
|
|
|
|
7 |
p. e342-e344
|
article
|
| 55 |
UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screening
|
Taylor-Phillips, Sian
|
|
|
|
7 |
p. e558-e565
|
article
|
| 56 |
Unicorns and cowboys in digital health: the importance of public perception
|
The Lancet Digital Health,
|
|
|
|
7 |
p. e319
|
article
|
| 57 |
Virtual primary care: fragmentation or integration?
|
Wharton, George A
|
|
|
|
7 |
p. e330-e331
|
article
|
| 58 |
Wearable technology and the cardiovascular system: the future of patient assessment
|
Williams, Gareth J
|
|
|
|
7 |
p. e467-e476
|
article
|
| 59 |
Will deep learning change outcomes in liver transplant?
|
Khorsandi, Shirin Elizabeth
|
|
|
|
7 |
p. e398-e399
|
article
|
| 60 |
Will the smartphone become a useful tool to promote physical activity?
|
Tison, Geoffrey H
|
|
|
|
7 |
p. e322-e323
|
article
|
Journal description