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AI and ML- based Deployments in Healthcare: Trends for 2019
The medical industry is today filled with AI, machine learning, and deep learning solutions. They have become tools that can help companies to healthcare providers to improve their service and the standard of care, generate higher income, and reduce risks. Artificial intelligence robots are increasingly helping microsurgical procedures to reduce surgical variations, which could affect patient recovery.
The healthcare industry already benefits from AI, machine learning and deep learning. For instance, AI systems will generate $6.7 billion in global health industry revenue by 2021, according to research firm Frost & Sullivan. Their annual growth rate in 2014 was only 634 million dollars, which is a 40 percent compound. AI-driven chatbots can influence healthcare worlds. The Juniper Research report indicates that chatbots are responsible for saving retail, electronic trade, banking, and healthcare $8 trillion per annum by 2022.
This is just the beginning. Healthcare industry is ready for AI transformation and is driven by a wide range of information sources-electronic mobile devices, health records, embedded sensors, genome sequences, and even billing documents. The principal material is required to implement AI and ML solutions, is the data. Data is an essential ingredient in improving efficiency and results for providers.
While the requirements of patients to be treated and new therapies develop are often relegated to a healthcare backburner by collection and analysis, new tools enable developers to easily incorporate ML and other capabilities into the routine development and delivery process. AI and ML have now been made available to everybody, not an exclusive province of researchers and technology companies.
The cloud provides the capacity to provide flexibility, security, and analytical capabilities required for the implementation of AI and ML to drive innovations. Cloud computing platforms make data, whether structured, unstructured or streamed, easier to inject and process. These tools simplify the development, training, and deployment of machine-based learning models. Healthcare organizations that use the data to improve their efficiency and effectiveness will be the most successful in the next few years, as they move forward to value-based care in conjunction with AI.