دورية أكاديمية
AI and Big Data: A New Paradigm for Decision Making in Healthcare
| العنوان: | AI and Big Data: A New Paradigm for Decision Making in Healthcare |
|---|---|
| المؤلفون: | Efthymiou, Iris - Panagiota, Vozikis, Athanassios, Sidiropoulos, Symeon, Kritas, Dimitrios |
| المصدر: | HAPSc Policy Briefs Series ; 1 ; 2 ; 138-145 |
| بيانات النشر: | MISC |
| سنة النشر: | 2021 |
| المجموعة: | SSOAR - Social Science Open Access Repository |
| مصطلحات موضوعية: | Medizin und Gesundheit, Naturwissenschaften, Medicine and health, Science, Big Data, artificial intelligence (AI), Clinical dependency, public sector opinion, Clinical Decision Support Systems, Decision Making, Healthcare, Human Resources Management, Key Performance Indicators (KPIs), medical diagnostic analysis decisions, Medizin, Sozialmedizin, Technik(wissenschaften), angewandte Wissenschaften, Medicine, Social Medicine, Natural Science and Engineering, Applied Sciences |
| الوقت: | 50100, 50200, 30100 |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| تدمد: | 2732-6578 |
| العلاقة: | https://www.ssoar.info/ssoar/handle/document/72206Test; https://doi.org/10.12681/hapscpbs.26490Test |
| DOI: | 10.12681/hapscpbs.26490 |
| الإتاحة: | https://doi.org/10.12681/hapscpbs.26490Test https://www.ssoar.info/ssoar/handle/document/72206Test |
| حقوق: | Creative Commons - Namensnennung, Nicht-kommerz. 4.0 ; Creative Commons - Attribution-NonCommercial 4.0 |
| رقم الانضمام: | edsbas.20EAE88C |
| قاعدة البيانات: | BASE |
| تدمد: | 27326578 |
|---|---|
| DOI: | 10.12681/hapscpbs.26490 |