| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A co-designed mHealth programme to support healthy lifestyles in Māori and Pasifika peoples in New Zealand (OL@-OR@): a cluster-randomised controlled trial
|
Ni Mhurchu, Cliona
|
|
2019
|
|
6 |
p. e298-e307
|
artikel
|
| 2 |
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
|
Liu, Xiaoxuan
|
|
2019
|
|
6 |
p. e271-e297
|
artikel
|
| 3 |
A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations
|
Sabanayagam, Charumathi
|
|
|
|
6 |
p. e295-e302
|
artikel
|
| 4 |
Advancing heart failure research using machine learning
|
Mohammad, Moman A
|
|
|
|
6 |
p. e331-e332
|
artikel
|
| 5 |
AI models in health care are not colour blind and we should not be either
|
Wiens, Jenna
|
|
|
|
6 |
p. e399-e400
|
artikel
|
| 6 |
AI recognition of patient race in medical imaging: a modelling study
|
Gichoya, Judy Wawira
|
|
|
|
6 |
p. e406-e414
|
artikel
|
| 7 |
An awakening in medicine: the partnership of humanity and intelligent machines
|
Celi, Leo Anthony
|
|
2019
|
|
6 |
p. e255-e257
|
artikel
|
| 8 |
A real-time dashboard of clinical trials for COVID-19
|
Thorlund, Kristian
|
|
|
|
6 |
p. e286-e287
|
artikel
|
| 9 |
Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review
|
Jones, O T
|
|
|
|
6 |
p. e466-e476
|
artikel
|
| 10 |
Assessing risk factors for SARS-CoV-2 infection in patients presenting with symptoms in Shanghai, China: a multicentre, observational cohort study
|
Mao, Bei
|
|
|
|
6 |
p. e323-e330
|
artikel
|
| 11 |
Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
|
Liu, Yonghong
|
|
|
|
6 |
p. e349-e359
|
artikel
|
| 12 |
Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank
|
Jiang, Rongtao
|
|
|
|
6 |
p. e350-e359
|
artikel
|
| 13 |
Big data and health
|
Snyder, Michael
|
|
2019
|
|
6 |
p. e252-e254
|
artikel
|
| 14 |
Big data and predictive modelling for the opioid crisis: existing research and future potential
|
Bharat, Chrianna
|
|
|
|
6 |
p. e397-e407
|
artikel
|
| 15 |
Big data, artificial intelligence, and the opioid crisis
|
The Lancet Digital Health,
|
|
|
|
6 |
p. e330
|
artikel
|
| 16 |
Building trust while influencing online COVID-19 content in the social media world
|
Limaye, Rupali Jayant
|
|
|
|
6 |
p. e277-e278
|
artikel
|
| 17 |
Changes in the incidence of invasive disease due to Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis during the COVID-19 pandemic in 26 countries and territories in the Invasive Respiratory Infection Surveillance Initiative: a prospective analysis of surveillance data
|
Brueggemann, Angela B
|
|
|
|
6 |
p. e360-e370
|
artikel
|
| 18 |
Clinical applications of continual learning machine learning
|
Lee, Cecilia S
|
|
|
|
6 |
p. e279-e281
|
artikel
|
| 19 |
Clinical ground truth in machine learning for early sepsis diagnosis
|
Lindner, Holger A
|
|
|
|
6 |
p. e338-e339
|
artikel
|
| 20 |
Comprehensive genomic profiling and treatment patterns across ancestries in advanced prostate cancer: a large-scale retrospective analysis
|
Sivakumar, Smruthy
|
|
|
|
6 |
p. e380-e389
|
artikel
|
| 21 |
Continual learning in medical devices: FDA's action plan and beyond
|
Vokinger, Kerstin N
|
|
|
|
6 |
p. e337-e338
|
artikel
|
| 22 |
Correction to Lancet Digital Health 2019; 1: e198–99
|
|
|
2019
|
|
6 |
p. e260
|
artikel
|
| 23 |
Correction to Lancet Digital Health 2020; published online May 6. https://doi.org/10.1016/S2589-7500(20)30104-7
|
|
|
|
|
6 |
p. e292
|
artikel
|
| 24 |
Correction to Lancet Digit Health 2022; 4: e384–97
|
|
|
|
|
6 |
p. e405
|
artikel
|
| 25 |
Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study
|
Areia, Miguel
|
|
|
|
6 |
p. e436-e444
|
artikel
|
| 26 |
COVID-19 containment measures and incidence of invasive bacterial disease
|
Smith, David R M
|
|
|
|
6 |
p. e331-e332
|
artikel
|
| 27 |
Decentralised clinical trials: ethical opportunities and challenges
|
Vayena, Effy
|
|
|
|
6 |
p. e390-e394
|
artikel
|
| 28 |
Deep learning: a turning point in acute neurology
|
Filippi, Massimo
|
|
|
|
6 |
p. e273-e274
|
artikel
|
| 29 |
Deep learning for pancreatic cancer detection: current challenges and future strategies
|
Chu, Linda C
|
|
|
|
6 |
p. e271-e272
|
artikel
|
| 30 |
Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation
|
Sjoding, Michael W
|
|
|
|
6 |
p. e340-e348
|
artikel
|
| 31 |
Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
|
Liu, Kao-Lang
|
|
|
|
6 |
p. e303-e313
|
artikel
|
| 32 |
Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study
|
Lo-Ciganic, Wei-Hsuan
|
|
|
|
6 |
p. e455-e465
|
artikel
|
| 33 |
Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study
|
Wagner, Siegfried K
|
|
|
|
6 |
p. e340-e349
|
artikel
|
| 34 |
Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study
|
Afshar, Majid
|
|
|
|
6 |
p. e426-e435
|
artikel
|
| 35 |
Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach
|
Leighton, Samuel P
|
|
2019
|
|
6 |
p. e261-e270
|
artikel
|
| 36 |
Digital health at the age of the Anthropocene
|
Chevance, Guillaume
|
|
|
|
6 |
p. e290-e291
|
artikel
|
| 37 |
Ethics of large language models in medicine and medical research
|
Li, Hanzhou
|
|
|
|
6 |
p. e333-e335
|
artikel
|
| 38 |
Ethnic bias in data linkage
|
Grath-Lone, Louise Mc
|
|
|
|
6 |
p. e339
|
artikel
|
| 39 |
Guarding a city from the COVID-19 pandemic
|
Xu, Jiuyang
|
|
|
|
6 |
p. e275-e276
|
artikel
|
| 40 |
Health information technology and digital innovation for national learning health and care systems
|
Sheikh, Aziz
|
|
|
|
6 |
p. e383-e396
|
artikel
|
| 41 |
Human versus machine in medicine: can scientific literature answer the question?
|
Cook, Tessa S
|
|
2019
|
|
6 |
p. e246-e247
|
artikel
|
| 42 |
Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study
|
Banerjee, Amitava
|
|
|
|
6 |
p. e370-e379
|
artikel
|
| 43 |
Innovative intelligent insole system reduces diabetic foot ulcer recurrence at plantar sites: a prospective, randomised, proof-of-concept study
|
Abbott, Caroline A
|
|
2019
|
|
6 |
p. e308-e318
|
artikel
|
| 44 |
Learning from community-led and co-designed m-health interventions
|
Duncan, Mitch J
|
|
2019
|
|
6 |
p. e248-e249
|
artikel
|
| 45 |
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study
|
Monteiro, Miguel
|
|
|
|
6 |
p. e314-e322
|
artikel
|
| 46 |
Natural language processing to identify substance misuse in the electronic health record
|
Riddick, Tyne A
|
|
|
|
6 |
p. e401-e402
|
artikel
|
| 47 |
Opportunistic deep learning of retinal photographs: the window to the body revisited
|
Waldstein, Sebastian M
|
|
|
|
6 |
p. e269-e270
|
artikel
|
| 48 |
Opportunities for opioid overdose prediction: building a population health approach
|
Allen, Bennett
|
|
|
|
6 |
p. e403-e404
|
artikel
|
| 49 |
Pandemic versus pandemonium: fighting on two fronts
|
The Lancet Digital Health,
|
|
|
|
6 |
p. e268
|
artikel
|
| 50 |
Predicting hospitalisation for heart failure and death in patients with, or at risk of, heart failure before first hospitalisation: a retrospective model development and external validation study
|
Bradley, Joshua
|
|
|
|
6 |
p. e445-e454
|
artikel
|
| 51 |
Preventing foot ulcers in diabetes using plantar pressure feedback
|
Bus, Sicco A
|
|
2019
|
|
6 |
p. e250-e251
|
artikel
|
| 52 |
Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study
|
Jiang, Yuming
|
|
|
|
6 |
p. e371-e382
|
artikel
|
| 53 |
Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data
|
Rasmy, Laila
|
|
|
|
6 |
p. e415-e425
|
artikel
|
| 54 |
Remote shared care delivery: a virtual response to COVID-19
|
Ramdas, Kamalini
|
|
|
|
6 |
p. e288-e289
|
artikel
|
| 55 |
Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial
|
Kann, Benjamin H
|
|
|
|
6 |
p. e360-e369
|
artikel
|
| 56 |
Shut down and reboot—preparing to minimise infection in a post-COVID-19 era
|
McCall, Becky
|
|
|
|
6 |
p. e293-e294
|
artikel
|
| 57 |
Steps in the right direction for physical frailty research
|
Cox, Simon R
|
|
|
|
6 |
p. e329-e330
|
artikel
|
| 58 |
The challenges and opportunities of mental health data sharing in the UK
|
Ford, Tamsin
|
|
|
|
6 |
p. e333-e336
|
artikel
|
| 59 |
Toward clinically useful models for individualised prognostication in psychosis
|
Koutsouleris, Nikolaos
|
|
2019
|
|
6 |
p. e244-e245
|
artikel
|
| 60 |
Turning the tide on the opioid crisis
|
The Lancet Digital Health,
|
|
|
|
6 |
p. e398
|
artikel
|
| 61 |
Twitter, public health, and misinformation
|
The Lancet Digital Health,
|
|
|
|
6 |
p. e328
|
artikel
|
| 62 |
Vaccine misinformation and social media
|
Burki, Talha
|
|
2019
|
|
6 |
p. e258-e259
|
artikel
|
| 63 |
Virtual care: new models of caring for our patients and workforce
|
Schwamm, Lee H
|
|
|
|
6 |
p. e282-e285
|
artikel
|
| 64 |
Wearable devices: underrepresentation in the ageing society
|
Guu, Ta-Wei
|
|
|
|
6 |
p. e336-e337
|
artikel
|
| 65 |
Wearable technology and lifestyle management: the fight against obesity and diabetes
|
The Lancet Digital Health,
|
|
2019
|
|
6 |
p. e243
|
artikel
|
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