| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A deep-learning-enabled diagnosis of ovarian cancer
|
Van Calster, Ben
|
|
|
|
9 |
p. e630
|
artikel
|
| 2 |
A deep-learning-enabled diagnosis of ovarian cancer – Authors' reply
|
Gao, Yue
|
|
|
|
9 |
p. e631
|
artikel
|
| 3 |
Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study
|
Sudre, Carole H
|
|
|
|
9 |
p. e577-e586
|
artikel
|
| 4 |
Approaching autonomy in medical artificial intelligence
|
Bitterman, Danielle S
|
|
|
|
9 |
p. e447-e449
|
artikel
|
| 5 |
Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints
|
Oren, Ohad
|
|
|
|
9 |
p. e486-e488
|
artikel
|
| 6 |
Can AI technologies close the diagnostic gap in tuberculosis?
|
Tzelios, Christine
|
|
|
|
9 |
p. e535-e536
|
artikel
|
| 7 |
Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study
|
Hosny, Ahmed
|
|
|
|
9 |
p. e657-e666
|
artikel
|
| 8 |
Convolutional neural network for the detection of pancreatic cancer on CT scans
|
Suman, Garima
|
|
|
|
9 |
p. e453
|
artikel
|
| 9 |
Convolutional neural network for the detection of pancreatic cancer on CT scans – Authors' reply
|
Liao, Wei-Chih
|
|
|
|
9 |
p. e454
|
artikel
|
| 10 |
Correction to Lancet Digital Health 2020; 2: e94–101
|
|
|
|
|
9 |
p. e455
|
artikel
|
| 11 |
Correction to Lancet Digital Health 2020; 2: e348–57
|
|
|
|
|
9 |
p. e455
|
artikel
|
| 12 |
Correction to Lancet Digit Health 2021; 3: e577–86
|
|
|
|
|
9 |
p. e542
|
artikel
|
| 13 |
Correction to Lancet Digit Health 2023; 5: e446–57
|
|
|
|
|
9 |
p. e550
|
artikel
|
| 14 |
Could telemedicine solve the cancer backlog?
|
McCall, Becky
|
|
|
|
9 |
p. e456-e457
|
artikel
|
| 15 |
COVID-19 detection from audio: seven grains of salt
|
Coppock, Harry
|
|
|
|
9 |
p. e537-e538
|
artikel
|
| 16 |
COVID-19: digital equivalence of health care in English prisons
|
Edge, Chantal
|
|
|
|
9 |
p. e450-e452
|
artikel
|
| 17 |
Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
|
Byeon, Seul Kee
|
|
|
|
9 |
p. e632-e645
|
artikel
|
| 18 |
Digital health funding for COVID-19 vaccine deployment across four major donor agencies
|
Helldén, Daniel
|
|
|
|
9 |
p. e627-e631
|
artikel
|
| 19 |
Digital solutions for early breast cancer detection
|
The Lancet Digital Health,
|
|
|
|
9 |
p. e545
|
artikel
|
| 20 |
Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning
|
Severson, Kristen A
|
|
|
|
9 |
p. e555-e564
|
artikel
|
| 21 |
Do I sound sick?
|
The Lancet Digital Health,
|
|
|
|
9 |
p. e534
|
artikel
|
| 22 |
Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study
|
Canas, Liane S
|
|
|
|
9 |
p. e587-e598
|
artikel
|
| 23 |
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
|
Dembrower, Karin
|
|
|
|
9 |
p. e468-e474
|
artikel
|
| 24 |
Effect of COVID-19 vaccination and booster on maternal–fetal outcomes: a retrospective cohort study
|
Piekos, Samantha N
|
|
|
|
9 |
p. e594-e606
|
artikel
|
| 25 |
Efficacy of telemedicine for the management of cardiovascular disease: a systematic review and meta-analysis
|
Kuan, Pei Xuan
|
|
|
|
9 |
p. e676-e691
|
artikel
|
| 26 |
FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks
|
Muehlematter, Urs J
|
|
|
|
9 |
p. e618-e626
|
artikel
|
| 27 |
Harnessing wearables and mobile phones to improve glycemic outcomes with automated insulin delivery
|
Lewis, Dana M
|
|
|
|
9 |
p. e548-e549
|
artikel
|
| 28 |
Improving the health of young African American women in the preconception period using health information technology: a randomised controlled trial
|
Jack, Brian W
|
|
|
|
9 |
p. e475-e485
|
artikel
|
| 29 |
Inferring pain experience in infants using quantitative whole-brain functional MRI signatures: a cross-sectional, observational study
|
Duff, Eugene P
|
|
|
|
9 |
p. e458-e467
|
artikel
|
| 30 |
Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial
|
Jacobs, Peter G
|
|
|
|
9 |
p. e607-e617
|
artikel
|
| 31 |
Menstrual irregularities and vaginal bleeding after COVID-19 vaccination reported to v-safe active surveillance, USA in December, 2020–January, 2022: an observational cohort study
|
Wong, Karen K
|
|
|
|
9 |
p. e667-e675
|
artikel
|
| 32 |
My body, my choice, my data
|
The Lancet Digital Health,
|
|
|
|
9 |
p. e627
|
artikel
|
| 33 |
Pain in the newborn brain: a neural signature
|
Duerden, Emma G
|
|
|
|
9 |
p. e442-e443
|
artikel
|
| 34 |
Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study
|
Tang, Ruijie
|
|
|
|
9 |
p. e560-e570
|
artikel
|
| 35 |
Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review
|
Young, Albert T
|
|
|
|
9 |
p. e599-e611
|
artikel
|
| 36 |
Predicted COVID-19 positive cases, hospitalisations, and deaths associated with the Delta variant of concern, June–July, 2021
|
Shah, Syed Ahmar
|
|
|
|
9 |
p. e539-e541
|
artikel
|
| 37 |
Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study
|
Clift, Ash Kieran
|
|
|
|
9 |
p. e571-e581
|
artikel
|
| 38 |
Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort
|
Pamporaki, Christina
|
|
|
|
9 |
p. e551-e559
|
artikel
|
| 39 |
Public perceptions on data sharing: key insights from the UK and the USA
|
Ghafur, Saira
|
|
|
|
9 |
p. e444-e446
|
artikel
|
| 40 |
Reinforcement learning in ophthalmology: potential applications and challenges to implementation
|
Nath, Siddharth
|
|
|
|
9 |
p. e692-e697
|
artikel
|
| 41 |
Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies
|
Espinosa-Gonzalez, Ana
|
|
|
|
9 |
p. e646-e656
|
artikel
|
| 42 |
Risk stratification of patients with COVID-19 in the community
|
Knight, Stephen R
|
|
|
|
9 |
p. e628-e629
|
artikel
|
| 43 |
The myth of generalisability in clinical research and machine learning in health care
|
Futoma, Joseph
|
|
|
|
9 |
p. e489-e492
|
artikel
|
| 44 |
The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study
|
Zauderer, Marjorie G
|
|
|
|
9 |
p. e565-e576
|
artikel
|
| 45 |
Transparency during global health emergencies
|
The Lancet Digital Health,
|
|
|
|
9 |
p. e441
|
artikel
|
| 46 |
Trends in invasive bacterial diseases during the first 2 years of the COVID-19 pandemic: analyses of prospective surveillance data from 30 countries and territories in the IRIS Consortium
|
Shaw, David
|
|
|
|
9 |
p. e582-e593
|
artikel
|
| 47 |
Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms
|
Qin, Zhi Zhen
|
|
|
|
9 |
p. e543-e554
|
artikel
|
| 48 |
Vaccines for pregnant people: are we missing the forest for the trees?
|
Metz, Torri D
|
|
|
|
9 |
p. e546-e547
|
artikel
|
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