| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study
|
Babenko, Boris
|
|
|
|
5 |
p. e257-e264
|
artikel
|
| 2 |
Africa: opportunities for growth
|
The Lancet Digital Health,
|
|
|
|
5 |
p. e193
|
artikel
|
| 3 |
AI for identification of systemic biomarkers from external eye photos: a promising field in the oculomics revolution
|
DeBuc, Delia Cabrera
|
|
|
|
5 |
p. e249-e250
|
artikel
|
| 4 |
A machine learning-based screening tool for genetic syndromes in children
|
Mensah, Martin Atta
|
|
|
|
5 |
p. e295
|
artikel
|
| 5 |
A machine learning-based screening tool for genetic syndromes in children – Authors' reply
|
Porras, Antonio R
|
|
|
|
5 |
p. e296
|
artikel
|
| 6 |
A new paradigm for drug development
|
Burki, Talha
|
|
|
|
5 |
p. e226-e227
|
artikel
|
| 7 |
Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study
|
Xie, Yuchen
|
|
|
|
5 |
p. e240-e249
|
artikel
|
| 8 |
Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study
|
Tolkach, Yuri
|
|
|
|
5 |
p. e265-e275
|
artikel
|
| 9 |
Augmenting digital twins with federated learning in medicine
|
Nagaraj, Divya
|
|
|
|
5 |
p. e251-e253
|
artikel
|
| 10 |
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
|
Faes, Livia
|
|
|
|
5 |
p. e232-e242
|
artikel
|
| 11 |
Automated machine learning as a partner in predictive modelling
|
Callender, Thomas
|
|
|
|
5 |
p. e254-e256
|
artikel
|
| 12 |
Bridging the digital divide in health care
|
Makri, Anita
|
|
|
|
5 |
p. e204-e205
|
artikel
|
| 13 |
Can technology help improve diarrhoea management?
|
Bhutta, Zulfiqar A
|
|
|
|
5 |
p. e214-e215
|
artikel
|
| 14 |
Can technology increase COVID-19 vaccination rates?
|
The Lancet Digital Health,
|
|
|
|
5 |
p. e274
|
artikel
|
| 15 |
Cardiology and big data: a call for papers
|
Spencer, Stuart
|
|
|
|
5 |
p. e277
|
artikel
|
| 16 |
Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence
|
Laghi, Andrea
|
|
|
|
5 |
p. e225
|
artikel
|
| 17 |
Children must co-design digital health research
|
The Lancet Digital Health,
|
|
|
|
5 |
p. e248
|
artikel
|
| 18 |
Correction to Lancet Digital Health 2020; 2: e229–239
|
|
|
|
|
5 |
p. e228
|
artikel
|
| 19 |
Correction to Lancet Digit Health 2021; 3: 286–94
|
|
|
|
|
5 |
p. e283
|
artikel
|
| 20 |
Correction to Lancet Digit Health 2022; 4: e256–65
|
|
|
|
|
5 |
p. e299
|
artikel
|
| 21 |
Covid-19 vaccine apps should deliver more to patients
|
Dasgupta, Nabarun
|
|
|
|
5 |
p. e278-e279
|
artikel
|
| 22 |
Cyber risks to Ukrainian and other health systems
|
Samarasekera, Udani
|
|
|
|
5 |
p. e297-e298
|
artikel
|
| 23 |
Data, data all around
|
McCall, Becky
|
|
|
|
5 |
p. e284-e285
|
artikel
|
| 24 |
Data sharing in the era of COVID-19
|
Cosgriff, Christopher V
|
|
|
|
5 |
p. e224
|
artikel
|
| 25 |
Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs
|
Rim, Tyler Hyungtaek
|
|
|
|
5 |
p. e306-e316
|
artikel
|
| 26 |
Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study
|
Archer, Derek B
|
|
|
|
5 |
p. e222-e231
|
artikel
|
| 27 |
Digital health and telehealth in cancer care: a scoping review of reviews
|
Shaffer, Kelly M
|
|
|
|
5 |
p. e316-e327
|
artikel
|
| 28 |
Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study
|
Raket, Lars Lau
|
|
|
|
5 |
p. e229-e239
|
artikel
|
| 29 |
Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis
|
Champion, Katrina E
|
|
|
|
5 |
p. e206-e221
|
artikel
|
| 30 |
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
|
Sin, Jacqueline
|
|
|
|
5 |
p. e320-e329
|
artikel
|
| 31 |
Electronic decision support and diarrhoeal disease guideline adherence (mHDM): a cluster randomised controlled trial
|
Khan, Ashraful I
|
|
|
|
5 |
p. e250-e258
|
artikel
|
| 32 |
Ethical limitations of algorithmic fairness solutions in health care machine learning
|
McCradden, Melissa D
|
|
|
|
5 |
p. e221-e223
|
artikel
|
| 33 |
Health4Life eHealth intervention to modify multiple lifestyle risk behaviours among adolescent students in Australia: a cluster-randomised controlled trial
|
Champion, Katrina E
|
|
|
|
5 |
p. e276-e287
|
artikel
|
| 34 |
Holding artificial intelligence to account
|
The Lancet Digital Health,
|
|
|
|
5 |
p. e290
|
artikel
|
| 35 |
Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study
|
Faghri, Faraz
|
|
|
|
5 |
p. e359-e369
|
artikel
|
| 36 |
Improving community health-care screenings with smartphone-based AI technologies
|
Mantena, Sreekar
|
|
|
|
5 |
p. e280-e282
|
artikel
|
| 37 |
Improving eHealth intervention development and quality of evaluations
|
Henderson, Marion
|
|
|
|
5 |
p. e194-e195
|
artikel
|
| 38 |
Improving epidemic surveillance and response: big data is dead, long live big data
|
Buckee, Caroline
|
|
|
|
5 |
p. e218-e220
|
artikel
|
| 39 |
Improving parkinsonism diagnosis with machine learning
|
Abel, Shawna
|
|
|
|
5 |
p. e196-e197
|
artikel
|
| 40 |
Is large-scale population screening coming to psychiatry?
|
Cristea, Ioana Alina
|
|
|
|
5 |
p. e210-e211
|
artikel
|
| 41 |
Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data
|
Nitski, Osvald
|
|
|
|
5 |
p. e295-e305
|
artikel
|
| 42 |
Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study
|
Wang, Shuo
|
|
|
|
5 |
p. e309-e319
|
artikel
|
| 43 |
Moving forward with machine learning models in acute chest pain
|
Ekelund, Ulf
|
|
|
|
5 |
p. e291-e292
|
artikel
|
| 44 |
Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study
|
Jiang, Yuming
|
|
|
|
5 |
p. e340-e350
|
artikel
|
| 45 |
Predicting peritoneal recurrence by artificial intelligence
|
Terashima, Masanori
|
|
|
|
5 |
p. e293-e294
|
artikel
|
| 46 |
Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study
|
Jiao, Zhicheng
|
|
|
|
5 |
p. e286-e294
|
artikel
|
| 47 |
Progress in examining cost-effectiveness of AI in diabetic retinopathy screening
|
Dismuke, Clara
|
|
|
|
5 |
p. e212-e213
|
artikel
|
| 48 |
Readiness for implementation of novel digital health interventions for postoperative monitoring: a systematic review and clinical innovation network analysis
|
McLean, Kenneth A
|
|
|
|
5 |
p. e295-e315
|
artikel
|
| 49 |
Reconceptualising the digital maturity of health systems
|
Cresswell, Kathrin
|
|
|
|
5 |
p. e200-e201
|
artikel
|
| 50 |
Reflecting on a future ready for digital health
|
The Lancet Digital Health,
|
|
|
|
5 |
p. e209
|
artikel
|
| 51 |
Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study
|
Tan, Tien-En
|
|
|
|
5 |
p. e317-e329
|
artikel
|
| 52 |
Screening for chronic obstructive pulmonary disease with artificial intelligence
|
Bibault, Jean-Emmanuel
|
|
|
|
5 |
p. e216-e217
|
artikel
|
| 53 |
Snakebite and snake identification: empowering neglected communities and health-care providers with AI
|
de Castañeda, Rafael Ruiz
|
|
|
|
5 |
p. e202-e203
|
artikel
|
| 54 |
The impact of commercial health datasets on medical research and health-care algorithms
|
Alberto, Isabelle Rose I
|
|
|
|
5 |
p. e288-e294
|
artikel
|
| 55 |
The medical algorithmic audit
|
Liu, Xiaoxuan
|
|
|
|
5 |
p. e384-e397
|
artikel
|
| 56 |
The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review
|
Mitratza, Marianna
|
|
|
|
5 |
p. e370-e383
|
artikel
|
| 57 |
Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT
|
Tang, Lisa Y W
|
|
|
|
5 |
p. e259-e267
|
artikel
|
| 58 |
Turning the crank for machine learning: ease, at what expense?
|
Pollard, Tom J
|
|
|
|
5 |
p. e198-e199
|
artikel
|
| 59 |
Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study
|
Oakden-Rayner, Lauren
|
|
|
|
5 |
p. e351-e358
|
artikel
|
| 60 |
Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge
|
Combalia, Marc
|
|
|
|
5 |
p. e330-e339
|
artikel
|
| 61 |
Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis
|
Doudesis, Dimitrios
|
|
|
|
5 |
p. e300-e308
|
artikel
|
| 62 |
Who does the model learn from?
|
Charpignon, Marie-Laure
|
|
|
|
5 |
p. e275-e276
|
artikel
|
Tijdschrift beschrijving