Emergency Medicine Australasia · 2018 · 168 citations · 42 references
ComputingComputer ScienceEmergency MedicineArtificial IntelligenceClinical Decision SupportMachine LearningDeep LearningNeural NetworksMachine Learning Decision SupportMachine Learning For Health MonitoringMachine Learning For HealthcareOpacityPatient Data SecurityPhysician Performance MeasurementSecurity Of Ai SystemsTrustworthy Ai
Artificial intelligence research has rapidly expanded, driven by deep learning successes, large datasets, and computing power, yet concerns about opacity, trust, and data security remain as AI integration into emergency medicine is anticipated. This perspective reviews current AI research applicable to emergency medicine.
Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.
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