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 |