Towards Artificial and Human Intelligence in Hybrid Healthcare

Abstract

Artificial Intelligence (AI) in hybrid healthcare has a wide application that begins with the application of machine learning (ML) algorithms and incorporates different cognitive technologies in either a research or clinical medical settings. The ultimate goal of AI is allowing patient portals, electronic health records, and even diagnostic and therapeutic equipment to not only mimic human cognition, but eventually one day surpass all of humanity’s cognitive abilities combined. Indeed, the most elementary use case of AI in healthcare is the use of ML and other cognitive disciplines for diagnosing diseases and then mapping those diagnostic outcomes with a contemporary (or perhaps even a novel or tailored) treatment plan. Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. In the hybrid healthcare context, this means gathering all the data (ever) recorded in patient Electronic Health Records (EHRs) and forming a digital knowledge base that includes various patient demographic, personal and clinical data, as well as all the past diagnoses, clinical decisions and treatment outcomes. The dream potential is that such AI systems will one day unlock a new era of personalized medicine that would go beyond the capability of an entire task force of specialists. As patient data skyrockets into the superfluous stratosphere of deep data lakes, AI will be as essential a tool to a doctor as the stethoscope once was.KeywordsArtificial Intelligence (AI)Machine Learning (ML)Deep LearningAlgorithmsVirtual HealthcareConvolution Neural Networks (CNNs)Swarm IntelligenceNatural Language Processing (NLP)Digital Medicine and Wearable Technology