AI and Big Data: A New Paradigm for Decision Making in Healthcare

Autor: Athanassios Vozikis, Iris Panagiota Efthymiou, Dimitrios Kritas, Symeon Sidiropoulos
Rok vydání: 2020
Předmět:
Big Data
Knowledge management
medical diagnostic analysis decisions
Computer science
Process (engineering)
Science
Big data
Naturwissenschaften
key performance indicators
Context (language use)
Clinical decision support system
decision making
Artificial Intelligence
ddc:610
Medicine
Social Medicine
Human Resources Management
Natural Science and Engineering
Applied Sciences
healthcare organizations
Medizin und Gesundheit
business.industry
Liability
healthcare
Medical Research
artificial intelligence (AI)
Clinical dependency
public sector opinion
Clinical Decision Support Systems
Decision Making
Healthcare
Key Performance Indicators (KPIs)
Medizin
Sozialmedizin
Naturwissenschaften
Technik(wissenschaften)
angewandte Wissenschaften
Knowledge base
AI
Human resource management
Medicine and health
ddc:500
Performance indicator
business
HRM
Zdroj: HAPSc Policy Briefs Series; Τόμ. 1 Αρ. 2 (2020): HAPSc Policy Briefs Series; 138-145
HAPSc Policy Briefs Series; Vol. 1 No. 2 (2020): HAPSc Policy Briefs Series; 138-145
HAPSc Policy Briefs Series
ISSN: 2732-6586
2732-6578
DOI: 10.12681/hapscpbs.26490
Popis: The latest developments in artificial intelligence (AI) - a general-purpose technology impacting many industries - have been based on advancements in machine learning, which is recast as a quality-adjusted decline in forecasting ratio. The influence of Policy on AI and big data has impacted two key magnitudes which are known as diffusion and consequences. And these must be focused primarily on the context of AI and big data. First, in addition to the policies on subsidies and intellectual property (IP) that will affect the propagation of AI in ways close to their effect on other technologies, three policy categories - privacy, exchange, and liability - may have a specific impact on the diffusion of AI. The first step in the prohibition process is to identify the shortcomings of current hospital procedures, why we need acute care AI, and eventually how the direction of patient decision-making will shift with the introduction of AI-based research. The second step is to establish a plan to shift the direction of medical education in order to enable physicians to retain control of AI. Medical research would need to rely less on threshold decision-making and more on the prediction, interpretation, and pathophysiological context of contextual time cycles. This should be an early part of a medical student's education, and this is what their hospital aid (AI) ought to do. Effective contact between human and artificial intelligence includes a shared pattern of focused knowledge base. Human-to-human contact protection in hospitals should lead this professional transformation process.
Databáze: OpenAIRE
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