Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2020
OBJECTIVE: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design ...
AIM: To assess the attitude about the importance of introducing education on artificial intelligence (AI) in medical schools' curricula among physicians whose everyday job is significantly impacted by AI.
Journal of the American Medical Informatics Association : JAMIA
Jul 1, 2020
OBJECTIVE: To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of curr...
BACKGROUND: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in rad...
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
OBJECTIVE: Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outp...
Annals of the American Thoracic Society
Mar 1, 2020
Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well-led IHCA resuscitation teams deliver better care, but expert resuscitation leaders are often unavai...
Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen
Jan 1, 2020
BACKGROUND: The digitalization process is currently on everyone's lips and sweeping changes in the field of public health and especially in surgery are to be expected within the next few years. Besides general issues, such as electronic health record...
OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
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