The biggest efforts here are to improve patient outcomes and reduce costs. Companies are applying machine learning to make more accurate and faster diagnoses than humans. One of the most well-known technologies in healthcare is IBM Watson. It understands natural language and is able to answer questions. The system condenses patient data and other available data sources into a hypothesis, which it then presents with a confidence scoring scheme. Other AI applications include chatbots, a computer program used online to answer questions and help patients schedule follow-up appointments or assist them with billing processes, and virtual health assistants that provide basic medical feedback.
"Healthcare will increase its IT efforts around intelligent COVID-19 sensor prediction and response systems, laying the foundation for better management of new disease outbreaks and ongoing population health needs."

"Automating processes through new technologies will be key to minimizing disruption and replacing workers when their presence is not possible." Robotic process automation is used for repetitive tasks that are normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information about how to better serve customers. Chatbots have been integrated into websites to provide immediate service to customers.

"Schools and universities are investing more in collaboration tools and end-user devices." Artificial intelligence can automate grading, for example, giving teachers more time. AI can assess students, adapt to their needs, and help them work at their own pace. AI tutors can provide additional support to students to ensure they stay "on track." AI can change where and how students learn in the long run.

Artificial intelligence is on the rise in financial institutions. AI financial applications, for example, can collect personal data and provide financial advice. Other programs, including IBM Watson, have already been applied to the process of buying a house. Today, software performs much of the trading on the stock market.

Sifting through documents is often a tedious process for humans. Automating this process saves time and creates more efficient processes. Startups are also building question-and-answer computer wizards that can sift through programmed questions by examining the taxonomy and ontology associated with a database.

This area is leading the way in integrating robots into the workflow. In the past, industrial robots performed only individual tasks and were separated from human workers. However, as technology advanced, this changed and robots are taking over more and more comprehensive processes in manufacturing.

Automation is the process by which a system or process operates automatically. For example, Robotic Process Automation (RPA) can be programmed to perform high-volume, repeatable tasks that are normally performed by humans. RPA differs from IT automation in that it can adapt to changing circumstances.

Machine Learning is the science of making a computer act without programming. Deep learning is a subfield of machine learning and can be understood as an automation of predictive analytics. There are three types of machine learning algorithms:​​​​​​​

• Supervised learning, in which datasets are labeled so that patterns are recognized and used to label new datasets;
• Unsupervised learning, in which data sets are unlabeled and sorted by similarities or differences.
• Reinforcement learning, in which data sets are not labeled but feedback is provided to the AI system after one or more actions.

Machine Vision is the science of making computers "see". Machine Vision captures and analyzes visual information using cameras, analog-to-digital conversion and digital signal processing. It is often compared to human vision, but Machine Vision is not tied to biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which focuses on machine vision, is often combined with machine vision.

Natural Language Processing (NLP) is the processing of human language by a computer program. One of the oldest and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides whether it is junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis, and speech recognition.

Pattern recognition is a branch of machine learning that focuses on identifying patterns in data.

Robotics is a field of mechanical engineering that focuses on the design and manufacture of robots. Robots are often used to perform tasks that are difficult or impossible for humans to accomplish. They are used in assembly lines for automobile production or in space travel to move large objects in space. More recently, researchers are using machine learning to build robots that can interact in social environments.