Last Update Date: Sep 30, 2019
- Data sharing is key to innovation in health care
MIT Tech Review, September 27, 2019 - An awakening in medicine: the partnership of humanity and intelligent machines
LA Celi et al, Lancet Digital Health, September 27, 2019 - Digital health: From clinical trials to diagnosis and surgery, artificial intelligence has the potential to transform medicine.
R Hodson, Nature Outlook, September 25, 2019 - A fairer way forward for AI in health care
L Nordling, Nature Outlook, September 25, 2019 - The future of electronic health records
J Hecht, Nature Outlook, September 25, 2019 - Deep learning algorithm predicts diabetic retinopathy progression in individual patients
F Arcadu et al, NPJ Digital Medicine, September 20, 2019 - Medical device surveillance with electronic health records
A Callahan et al, NPJ Digital Medicine, September 25, 2019 - Feasibility and utility of a clinician dashboard from wearable and mobile application Parkinson’s disease data
JJ Elm et al, NPJ Digital Medicine, September 25, 2019 - Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
The CONSORT-AI and SPIRIT-AI Steering Group, Nature Medicine, September 25, 2019 - Human versus machine in medicine: can scientific literature answer the question?
TS Cook, Lancet Digital Health, September 24, 2019 - A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.
Bechel Meagan A et al. PloS one 2019 14(9) e0222826 - Symptom-specific effectiveness of an internet-based intervention in the treatment of mild to moderate depressive symptomatology: The potential of network estimation techniques.
Boschloo Lynn et al. Behaviour research and therapy 2019 Aug 122103440 - Machine Learning Models Identify Multimodal Measurements Highly Predictive of Transdiagnostic Symptom Severity for Mood, Anhedonia, and Anxiety.
Mellem Monika S et al. Biological psychiatry. Cognitive neuroscience and neuroimaging 2019 Jul - Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.
Bao Yujia et al. JCO clinical cancer informatics 2019 Sep 31-9 - [Artificial intelligence: a benefit for patients?]
Marsico Giovanna et al. Soins; la revue de reference infirmiere 2019 Sep 64(838) 40-41 - Big Data for Nutrition Research in Pediatric Oncology: Current State and Framework for Advancement.
Phillips Charles A et al. Journal of the National Cancer Institute. Monographs 2019 Sep 2019(54) 127-131 - Machine learning in psychiatry- standards and guidelines.
Tandon Neeraj et al. Asian journal of psychiatry 2019 Sep - Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records.
Wu Chi-Shin et al. Journal of affective disorders 2019 Sep 260617-623 - Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.
Ghavami Nooshin et al. Medical image analysis 2019 Sep 58101558 - Machine learning for radiomics-based multi-modality and multi-parametric modeling.
Wei Lise et al. The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of... 2019 Sep
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