| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Accelerating health information technology capabilities across England's National Health Service
|
Cresswell, Kathrin
|
|
|
|
12 |
p. e758-e759
|
artikel
|
| 2 |
Addressing bias: artificial intelligence in cardiovascular medicine
|
Tat, Emily
|
|
|
|
12 |
p. e635-e636
|
artikel
|
| 3 |
A deep learning system for detection of early Barrett's neoplasia: a model development and validation study
|
Fockens, K N
|
|
|
|
12 |
p. e905-e916
|
artikel
|
| 4 |
An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation
|
Moghadam, Saeed Montazeri
|
|
|
|
12 |
p. e884-e892
|
artikel
|
| 5 |
Are digital devices a new risk factor for myopia?
|
Loughman, James
|
|
|
|
12 |
p. e756-e757
|
artikel
|
| 6 |
Artificial intelligence in COVID-19 drug repurposing
|
Zhou, Yadi
|
|
|
|
12 |
p. e667-e676
|
artikel
|
| 7 |
Association between digital smart device use and myopia: a systematic review and meta-analysis
|
Foreman, Joshua
|
|
|
|
12 |
p. e806-e818
|
artikel
|
| 8 |
Automated external defibrillator drones and their role in emergency response
|
Scholz, Sean S
|
|
|
|
12 |
p. e849-e850
|
artikel
|
| 9 |
Bias and privacy in AI's cough-based COVID-19 recognition
|
Perez-Espinosa, Humberto
|
|
|
|
12 |
p. e760
|
artikel
|
| 10 |
Bias and privacy in AI's cough-based COVID-19 recognition – Authors' reply
|
Coppock, Harry
|
|
|
|
12 |
p. e761
|
artikel
|
| 11 |
Blockchain applications in health care for COVID-19 and beyond: a systematic review
|
Ng, Wei Yan
|
|
|
|
12 |
p. e819-e829
|
artikel
|
| 12 |
Blockchain for increased trust in observational studies
|
Benchoufi, Mehdi
|
|
|
|
12 |
p. e762
|
artikel
|
| 13 |
Charting a functional brain growth curve to track early neurodevelopment
|
Slater, Rebeccah
|
|
|
|
12 |
p. e853-e854
|
artikel
|
| 14 |
COP27 Climate Change Conference: urgent action needed for Africa and the world
|
Atwoli, Lukoye
|
|
|
|
12 |
p. e858-e860
|
artikel
|
| 15 |
Correction to Lancet Digit Health 2020; 2: e395–96
|
|
|
|
|
12 |
p. e861
|
artikel
|
| 16 |
Correction to Lancet Digit Health 2022; 4: e497–506
|
|
|
|
|
12 |
p. e861
|
artikel
|
| 17 |
Correction to Lancet Digit Health 2020; 2: e573–81
|
|
|
|
|
12 |
p. e637
|
artikel
|
| 18 |
Curbing the carbon footprint of health care
|
The Lancet Digital Health,
|
|
|
|
12 |
p. e848
|
artikel
|
| 19 |
Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study
|
Jayachandran Preetha, Chandrakanth
|
|
|
|
12 |
p. e784-e794
|
artikel
|
| 20 |
Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study
|
Bilal, Mohsin
|
|
|
|
12 |
p. e763-e772
|
artikel
|
| 21 |
Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study
|
Dennis, John M
|
|
|
|
12 |
p. e873-e883
|
artikel
|
| 22 |
Drone delivery of automated external defibrillators compared with ambulance arrival in real-life suspected out-of-hospital cardiac arrests: a prospective observational study in Sweden
|
Schierbeck, Sofia
|
|
|
|
12 |
p. e862-e871
|
artikel
|
| 23 |
Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study
|
Raynaud, Marc
|
|
|
|
12 |
p. e795-e805
|
artikel
|
| 24 |
Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study
|
Wang, Xiaowan
|
|
|
|
12 |
p. e895-e904
|
artikel
|
| 25 |
Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis
|
Kendall, Michelle
|
|
|
|
12 |
p. e658-e666
|
artikel
|
| 26 |
Equitable precision medicine for type 2 diabetes
|
The Lancet Digital Health,
|
|
|
|
12 |
p. e850
|
artikel
|
| 27 |
Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study
|
Pullano, Giulia
|
|
|
|
12 |
p. e638-e649
|
artikel
|
| 28 |
Every breath you take, every move you make
|
The Lancet Digital Health,
|
|
|
|
12 |
p. e629
|
artikel
|
| 29 |
Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort
|
Fainberg, Hernan P
|
|
|
|
12 |
p. e862-e872
|
artikel
|
| 30 |
Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study
|
Natarajan, Aravind
|
|
|
|
12 |
p. e650-e657
|
artikel
|
| 31 |
Interpreting area under the receiver operating characteristic curve
|
de Hond, Anne A H
|
|
|
|
12 |
p. e853-e855
|
artikel
|
| 32 |
It's not easy being green
|
The Lancet Digital Health,
|
|
|
|
12 |
p. e751
|
artikel
|
| 33 |
Large language models and their impact in ophthalmology
|
Betzler, Bjorn Kaijun
|
|
|
|
12 |
p. e917-e924
|
artikel
|
| 34 |
Leveraging electronic health records for data science: common pitfalls and how to avoid them
|
Sauer, Christopher M
|
|
|
|
12 |
p. e893-e898
|
artikel
|
| 35 |
Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis
|
Smith, Luke A
|
|
|
|
12 |
p. e872-e881
|
artikel
|
| 36 |
Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study
|
Beaulieu-Jones, Brett K
|
|
|
|
12 |
p. e882-e894
|
artikel
|
| 37 |
Predicting seizure recurrence from medical records using large language models
|
Mbizvo, Gashirai K
|
|
|
|
12 |
p. e851-e852
|
artikel
|
| 38 |
Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening
|
Chalkidou, Anastasia
|
|
|
|
12 |
p. e899-e905
|
artikel
|
| 39 |
Risk of acute respiratory infection and acute cardiovascular events following acute respiratory infection among adults with increased cardiovascular risk in England between 2008 and 2018: a retrospective, population-based cohort study
|
Davidson, Jennifer A
|
|
|
|
12 |
p. e773-e783
|
artikel
|
| 40 |
Seeing more with less: virtual gadolinium-enhanced glioma imaging
|
Ferles, Alexandros
|
|
|
|
12 |
p. e754-e755
|
artikel
|
| 41 |
Sensor-aided continuous care and self-management: implications for the post-COVID era
|
Zhao, Megan
|
|
|
|
12 |
p. e632-e634
|
artikel
|
| 42 |
Standardising the role of a digital navigator in behavioural health: a systematic review
|
Perret, Sarah
|
|
|
|
12 |
p. e925-e932
|
artikel
|
| 43 |
The digital–physical divide for pathology research
|
Kohane, Isaac S
|
|
|
|
12 |
p. e859-e861
|
artikel
|
| 44 |
Time to reality check the promises of machine learning-powered precision medicine
|
Wilkinson, Jack
|
|
|
|
12 |
p. e677-e680
|
artikel
|
| 45 |
Towards better contact-tracing in the UK
|
Cook, Alex R
|
|
|
|
12 |
p. e630-e631
|
artikel
|
| 46 |
Towards computationally efficient prediction of molecular signatures from routine histology images
|
Lafarge, Maxime W
|
|
|
|
12 |
p. e752-e753
|
artikel
|
| 47 |
Treating type 2 diabetes: moving towards precision medicine
|
Yu, Oriana Hoi Yun
|
|
|
|
12 |
p. e851-e852
|
artikel
|
| 48 |
Using fine-tuned large language models to parse clinical notes in musculoskeletal pain disorders
|
Vaid, Akhil
|
|
|
|
12 |
p. e855-e858
|
artikel
|
| 49 |
Wearable devices—addressing bias and inequity
|
Zinzuwadia, Aniket
|
|
|
|
12 |
p. e856-e857
|
artikel
|
Tijdschrift beschrijving