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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.
The DHIS2 data model and platform are generic by design, not specifically tailored to the health context, to facilitate the application of DHIS2 to a variety of use cases. DHIS2 is a web-based platform. The core software and database are hosted on a server, which can be either physically located in the country of ownership or cloud-based.
Artificial intelligence is also starting to be used in video production, with tools and software being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Mid-journey, and Runway. [ 248 ]
A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are common examples of foundation models.
A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge and person-specific information to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow.
Studierfenster (StudierFenster) is a free, non-commercial Open Science client/server-based Medical Imaging Processing (MIP) online framework. [51] Medical open network for AI is a framework for Deep learning in healthcare imaging that is open-source available under the Apache Licence and supported by the community. [52]
The scale and capabilities of artificial intelligence (AI) systems are growing rapidly, notably due to advances in big data. In healthcare, it is expected to provide easier accessibility of information, and to improve treatments while reducing cost. The integration of AI in healthcare tends to improve the quality and efficiency of complex tasks.
A pioneer in the use of artificial intelligence in healthcare was American biomedical informatician Edward H. Shortliffe. This field deals with utilization of machine-learning algorithms and artificial intelligence, to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data.