What is technology-assisted decision-making used for?

Where and how is technology-assisted decision-making used?

Technology-assisted decision-making is used in a wide variety of fields, including finance, healthcare, marketing, and logistics, among others. It can help organisations and individuals make more informed and accurate decisions by providing them with the necessary information and analysis to evaluate different options and choose the best course of action.

However, it is important to note that technology-assisted decision-making is not a replacement for human decision-making. Rather, it is a tool that can support and enhance a human decision-making processes, helping individuals and organisations make better decisions faster and more efficiently.

Here are some examples of where and how it can be used:

1. Healthcare: In the field of healthcare, technology-assisted decision-making is being used to help doctors and clinicians make more accurate diagnoses and treatment decisions. For example, artificial intelligence powered diagnostic tools can analyse medical images, such as X-rays and MRI’s, to identify abnormalities that may be missed by the human eye. Technology-assisted decision-making is also being used to help hospitals optimise patient care by analysing patient data to identify trends and patterns, and make predictions about future health outcomes.
2. Financial trading: In the field of finance, technology-assisted decision-making is being used to help traders make better investment decisions. Artificial intelligence powered trading algorithms can analyse vast amounts of financial data in real-time to identify trends and patterns, and make predictions about future market movements. This can help traders make more informed and profitable investment decisions.
3. Environmental conservation: technology-assisted decision-making is being used to help monitor and manage wildlife populations and protect endangered species. One example of this is where artificial intelligence powered drones survey vast areas of land to identify and track animal populations, while machine learning algorithms analyse the data to identify areas where conservation efforts are needed most.
4. Transportation: technology-assisted decision-making is being used to optimise transportation networks and reduce traffic congestion. For example, cities can use data from traffic sensors and GPS devices to analyse traffic patterns and adjust traffic signals in real-time to improve traffic flow.