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Artificial intelligence in healthcare is the application of artificial intelligence (AI) to analyze and understand complex medical and healthcare data. In some cases, it can exceed or augment human capabilities by providing better or faster ways to diagnose, treat, or prevent disease.
Due to the exponential growth of information technologies and applicable models, including artificial intelligence and data mining, in addition to the access ever-more comprehensive data sets, new and better information analysis techniques have been created, based on their ability to learn.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Some of the problems tackled by CRI are: creation of data warehouses of health care data that can be used for research, support of data collection in clinical trials by the use of electronic data capture systems, streamlining ethical approvals and renewals (in US the responsible entity is the local institutional review board), maintenance of ...
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
The standards allow for easier 'interoperability' of healthcare data as it is shared and processed uniformly and consistently by the different systems. This allows clinical and non-clinical data to be shared more easily, theoretically improving patient care and health system performance. [1]
If the ability to exchange records between different EMR systems were perfected ("interoperability" [20]), it would facilitate the coordination of health care delivery in nonaffiliated health care facilities. In addition, data from an electronic system can be used anonymously for statistical reporting in matters such as quality improvement ...
A remote data entry (RDE) system is a computerized system designed for the collection of data in electronic format. The term is most commonly applied to a class of software used in the life sciences industry for collecting patient data from participants in clinical research studies—research of new drugs and/or medical devices.
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