Glossary

AI in Healthcare

What is AI in Healthcare?

AI in healthcare covers applications that support clinical staff and administration, for example in analysing medical images, documentation, appointment scheduling or evaluating research data. Such systems provide pointers and ease workloads, but they do not replace clinical judgement and do not promise cures. Many medical AI applications are subject to medical device regulation and strict data protection rules for health data. Example: software highlights conspicuous areas on X-rays for review by specialists.

Topics

Health
Medicine
Data protection

Try KI.ordo

Agents, flows and templates in one workspace – free 24-hour demo.

Start demo

← All terms

Related terms

Computer vision

Computer vision is a field of AI in which computers interpret images and video. Typical tasks include detecting and locating objects, reading text in images and measuring components. The technology is used, for example, in quality control in manufacturing, in driver assistance systems, in logistics and in medical imaging.

image analysis
deep learning
+1

Data protection regulations

Data protection regulations are legal rules governing the handling of personal data. In the EU the General Data Protection Regulation (GDPR) is the key framework; among other things it requires a legal basis for every processing activity, data minimisation, transparency towards data subjects and data processing agreements with service providers. When using AI, organisations need to clarify which data ends up in inputs, knowledge sources and logs, and where it is processed.

GDPR
data protection
+1

Explainability

Explainability (explainable AI, XAI) describes how well one can understand why an AI system reached a particular result. It covers methods that show which inputs influenced a decision most, as well as practical aids such as source references and logs. Explainability matters for trust, troubleshooting and regulation – for example when a bank has to justify why an application was declined.

XAI
Transparency
+1

Human-in-the-Loop

Human-in-the-loop describes workflows in which people step into an AI process at defined points: they review results, approve or correct them, or take over difficult cases. The principle reduces the risk of faulty output and keeps accountability with people, especially for decisions affecting individuals, money or legal matters. Example: an AI agent drafts replies to customer enquiries and a member of staff checks them before they are sent.

Approval
Oversight
+1