nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
Acknowledgments to our reviewers in 2022
|
|
|
|
6 |
C |
p. |
artikel |
2 |
AI in healthcare startups and special challenges
|
Young, Alan S. |
|
|
6 |
C |
p. |
artikel |
3 |
An automatic early screening system of eye diseases using UWF fundus images based on deep neural networks
|
Wang, Shubin |
|
|
6 |
C |
p. |
artikel |
4 |
An efficient image segmentation scheme for determination of cranial index in scaphocephalic patients
|
Sabeti, M. |
|
|
6 |
C |
p. |
artikel |
5 |
A new vision of a simple 1D Convolutional Neural Networks (1D-CNN) with Leaky-ReLU function for ECG abnormalities classification
|
Lakhdari, Kheira |
|
|
6 |
C |
p. |
artikel |
6 |
Artificial Intelligence (AI) to improve chronic pain care: Evidence of AI learning
|
Piette, John D. |
|
|
6 |
C |
p. |
artikel |
7 |
Artificial intelligence-based rapid on-site cytopathological evaluation for bronchoscopy examinations
|
Ai, Dilbar |
|
|
6 |
C |
p. |
artikel |
8 |
Artificial intelligence-enabled predictive model of progression from moderate to severe aortic stenosis
|
Moualla, Soundos K. |
|
|
6 |
C |
p. |
artikel |
9 |
Artificial intelligence in echocardiography to diagnose congenital heart disease and fetal echocardiography
|
Gearhart, Addison |
|
|
6 |
C |
p. |
artikel |
10 |
Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review
|
Wang, Minhong |
|
|
6 |
C |
p. |
artikel |
11 |
Artificial intelligence to assist physicians in identifying patients with severe aortic stenosis
|
Thomas, James D. |
|
|
6 |
C |
p. |
artikel |
12 |
Artificial neural network to classify cognitive impairment using gait and clinical variables
|
Zhou, Yuhan |
|
|
6 |
C |
p. |
artikel |
13 |
Automatic detection of decreased ejection fraction and left ventricular hypertrophy on 4D cardiac CTA: Use of artificial intelligence with transfer learning to facilitate multi-site operations
|
Rockenbach, Marcio A.B.C. |
|
|
6 |
C |
p. |
artikel |
14 |
Bridging the divide between data scientists and clinicians
|
Bastian, Grace |
|
|
6 |
C |
p. |
artikel |
15 |
Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic
|
Ghosheh, Ghadeer O. |
|
|
6 |
C |
p. |
artikel |
16 |
Computer-vision based method for quantifying rising from chair in Parkinson's disease patients
|
Morinan, Gareth |
|
|
6 |
C |
p. |
artikel |
17 |
Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections
|
Washington, Peter |
|
|
6 |
C |
p. |
artikel |
18 |
Deep learning-based risk classification and auxiliary diagnosis of macular edema
|
Wu, Tianzhu |
|
|
6 |
C |
p. |
artikel |
19 |
Digitizing paper based ECG files to foster deep learning based analysis of existing clinical datasets: An exploratory analysis
|
Adedinsewo, Demilade A. |
|
|
6 |
C |
p. |
artikel |
20 |
Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction
|
Liao, Wenjun |
|
|
6 |
C |
p. |
artikel |
21 |
Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study
|
Smit, J.M. |
|
|
6 |
C |
p. |
artikel |
22 |
Forecasting length of stay: Will it be clear or cloudy today?
|
Deng, Charles |
|
|
6 |
C |
p. |
artikel |
23 |
Harnessing machine learning to support evidence-based medicine: A pragmatic reconciliation framework
|
Abujaber, Ahmad A. |
|
|
6 |
C |
p. |
artikel |
24 |
Local integration of deep learning for advanced visualization in congenital heart disease surgical planning
|
Nainamalai, Varatharajan |
|
|
6 |
C |
p. |
artikel |
25 |
Machine learning models using mobile game play accurately classify children with autism
|
Deveau, Nicholas |
|
|
6 |
C |
p. |
artikel |
26 |
Model drift: When it can be a sign of success and when it can be an occult problem
|
Carter, Rickey E. |
|
|
6 |
C |
p. |
artikel |
27 |
Patent ductus arteriosus (PDA) detection in echocardiograms using deep learning
|
Lei, Howard |
|
|
6 |
C |
p. |
artikel |
28 |
Predicting prolonged length of stay in patients with traumatic brain injury: A machine learning approach
|
Abujaber, Ahmad |
|
|
6 |
C |
p. |
artikel |
29 |
Privacy aspects of direct-to-consumer artificial intelligence/machine learning health apps
|
Gerke, Sara |
|
|
6 |
C |
p. |
artikel |
30 |
Prospective clinical evaluation of a machine-learning trained algorithm for detection of arterial pressure transducer drop
|
Rinehart, Joseph |
|
|
6 |
C |
p. |
artikel |
31 |
The challenges of health inequities and AI
|
Moore, Candace Makeda |
|
|
6 |
C |
p. |
artikel |
32 |
The ethical considerations including inclusion and biases, data protection, and proper implementation among AI in radiology and potential implications
|
Martin, Clarissa |
|
|
6 |
C |
p. |
artikel |
33 |
Towards automation of transcranial Doppler ultrasound data in pediatric cerebral malaria
|
Zhang, Bo |
|
|
6 |
C |
p. |
artikel |
34 |
Twelve key challenges in medical machine learning and solutions
|
Ellis, Randall J. |
|
|
6 |
C |
p. |
artikel |
35 |
Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings
|
Hipolito Canario, Diego A. |
|
|
6 |
C |
p. |
artikel |
36 |
VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning
|
Awasthi, Raghav |
|
|
6 |
C |
p. |
artikel |