1. A method for reducing a medical error, comprising: receiving an input signal including an input status when a first worker device enters a zone; receiving an output signal including an exit status when a firs
Here, the authors develop a deep-learning algorithm to predict biomarkers from histopathological imaging in advanced urothelial cancer patients. This method detects suitable patients for targeted therapy clinical trials with a significant reduction in molecular testing, providing cost and time savings in re...
Using machine learning methods to analyze the fatigue status of medical security personnel and the factors influencing fatigue (such as BMI, gender, and wearing protective clothing working hours), with the goal of identifying the key factors contributing
Bates DW, Spell N, Cullen DJ, et al; Adverse Drug Events Prevention Study Group. The costs of adverse drug events in hospitalized patients. JAMA. 1997;277(4):307-311. doi:10.1001/jama.1997.03540280045032ArticlePubMedGoogle ScholarCrossref 8. Thomas EJ, Studdert DM, Newhouse JP, et al. ...
The spread of pre-trained predictive models and ad-hoc ML models for diagnostic support allowed to provide of several advances for both patients and medical, increasing both the rate of correct clinical diagnoses and prevention in people. As we have discussed above, in the literature, several ...
MedicalResearch.com Interview with:Tammy M. Brady, MD, PhD (she/her/hers) Vice Chair for Clinical Research, Dept of Pediatrics Associate Director, Welch Center for Prevention, Epidemiology, and Clinical Research Associate Professor of Pediatrics, Division of Pediatric Nephrology Medical Director, Pedia...
students and 67(30%) doctors agreed that AI implementation will reduce the errors in diagnosis while 6(2.4%) medical students and 9(4%) doctors strongly disagreed and 69(27.9%) medical students and 75(33.6%) doctors disagreed regarding the contribution of AI in diagnostic error reduction. The...
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization Qi Gao, Zilong Li, Junping Zhang, Yi Zhang, Hongming Shan [4th Apr., 2023] [arXiv, 2023] [Paper]DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models ...
There are also third-party remote platforms for collaborative medical treatment, such as remote medical consultation platform, to explore the prevention and control role of New Coronavirus’s remote medical treatment in epidemic areas [5]. With the development of collaborative medical services, medical...
New links are being forged between individual patient data and the information in digital libraries or the tools of computerized decision support.2 While the potential for ease of access and error reduction seems obvious, new technologies should be held to the same standards of evidence as new ...