6 AI Applications

How AI applications work

An AI application takes information as input and produces an output. It may interpret information, recognize patterns, make predictions, or generate content. Inputs can include text, images, sensor readings, and location data; outputs can include recommendations, classifications, forecasts, or suggested actions.

In many settings, AI supports people rather than making the final decision. The value of an application depends not only on model performance, but also on the quality and relevance of its data, careful evaluation, and responsible use.

Everyday tools

AI features appear in familiar tools. A maps app can use location and other information to personalize suggestions. Voice assistants can interpret spoken requests, while translation and writing tools can convert language between forms or draft text. Shopping, music, and video services may recommend items based on patterns in user activity.

These features can be useful, but recommendations may not fit a person's needs. Some features also depend on personal data and user settings.

Health care

In health care, AI can analyze medical images and other complex data to find patterns relevant to detection, diagnosis, or treatment. Some authorized medical devices analyze retinal images for signs of diabetic retinopathy, and other tools can assist with medical-image processing.

These outputs are intended to support care. They do not guarantee correct results or replace clinical judgment.

Transportation

Transportation systems can use AI to interpret camera and sensor data, estimate traffic conditions, and support traffic management. Video analytics, for example, can help identify locations where traffic conflicts or crashes may be more likely. AI is also used in research and development for automated vehicles and aircraft systems.

Because errors in transportation can affect public safety, these systems require careful testing and oversight.

Agriculture and the environment

AI can analyze satellite, drone, and ground-level images to monitor crop or forest health. Predictive models can also help estimate crop yields or anticipate risks such as drought, pests, and wildfires. Farmers and land managers can use these insights to decide where to investigate or direct resources, but a prediction's usefulness depends on the data and conditions it represents.

Science, energy, and buildings

Researchers use AI to search large datasets for patterns and to classify or analyze images. NASA describes using AI to analyze satellite imagery, support mission planning, and enable spacecraft to perform some tasks autonomously.

In energy systems, AI can help forecast renewable power generation and support grid planning and operations. In buildings, it can assist with design, operations, and maintenance. These applications can speed analysis or improve planning, but people still need to check whether the results suit the task.

From input to action

A common AI workflow moves from collected information to a reviewed decision:

1. Collect input: Gather information such as a medical image, traffic-camera video, or satellite photograph.

2. Analyze patterns: A trained model identifies features relevant to a defined task.

3. Produce an output: The system may label an image, estimate a risk, create a forecast, or suggest an action.

4. Review and act: A person or another system checks the output and decides what to do.

The output is not automatically the final decision. Review connects the model's result to the real task and situation.

Reliability and responsible use

AI performs best when its training and evaluation data are relevant to the real situation in which it is used. Outputs can still be inaccurate, uneven across different groups or conditions, or difficult to interpret. A result therefore needs to be considered in light of the application and the situation it represents.

Applications that affect health, safety, or essential services need appropriate validation, monitoring, privacy protections, and human oversight. For AI-enabled medical devices, the FDA considers intended use and risks and emphasizes safety across development and deployment.