Use of AI in Point-of-care Devices in Healthcare: Identifying the Critical Factors for Successful Applications

Abstract

The application of machine learning and natural language processing technology in healthcare and its use in assisting doctors and healthcare professionals has been researched and documented by authors during the last few years. LIU and others reviewing the latest applications of AI in medicine state that with the assistance of AI the time required for a diagnosis can be greatly reduced and the diagnostic efficiency can be significantly improved. They provide examples of applications of AI in radiology, pathology, endoscopy, ultrasonography, and biochemical examinations. Jiang and others have outlined the major disease areas that use AI tools as cancer, neurology, and cardiology. They cite the use of IBM Watson that has made promising progress in oncology and achieved high degree of accuracy in treatment recommendations. Ultrasound point-of-care devices have been in use for many years to support clinical decision-making at the point of care including the emergency departments offering simple, fast, and precise solutions. This research focuses on studying the use of AI-driven technology in enhancing the utility of these ultrasound point-of-care devices. The research aims to identify the factors that impact the successful application of AI in this field through in-depth interviews with developers and users of such devices.

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