AI Terms Physicians Should Know

This glossary summarises key AI-related terms that are frequently used in clinical practice, research, and healthcare software descriptions. The aim is a concise, technically sound orientation without unnecessary technical detail.

LLMs (Large Language Models)

• Generate text by calculating statistically likely word sequences, not by understanding meaning.

• Models such as ChatGPT, Claude, or Gemini are general-purpose language models, not medical systems.

ML (Machine Learning)

• Learns patterns from existing data and therefore inherits errors and biases present in those data.

• Performs only as well as the data on which it was trained.

NLP (Natural Language Processing)

• Enables software to recognise, analyse, and structure unstructured medical text.

• Forms the basis for allowing computers to “read” clinical notes and extract relevant information, for example for coding, quality analysis, or searching large text corpora.

ACI (Ambient Clinical Intelligence)

• Captures conversations between clinicians and patients and automatically generates draft documentation.

• The generated text often appears highly plausible but may contain subtle inaccuracies.

• Commonly used to pre-draft anamnesis and progress notes.

CDSS (Clinical Decision Support Systems)

• Provide alerts or warnings about potential risks without making decisions themselves.

• Typical use cases include drug-drug interaction warnings, risk alerts, or reminder systems.

Hallucination

• Refers to the generation of factually incorrect content that is linguistically convincing and therefore difficult to detect.

• Example:

“According to the ESC Guideline 2023, ivabradine is recommended as first-line therapy in stable angina.”

Sounds credible, but is fabricated.

Algorithmic Bias

• Occurs when training data underrepresent or distort certain patient groups.

• Leads to systematically poorer model performance for specific populations.

Model Drift / Dataset Shift

• Describes declining model performance as data or clinical contexts change over time.

• Causes include new guidelines, shifting patient populations, or changes in clinical workflows.

Human-in-the-Loop

• Means that AI outputs are reviewed by a human before being used.

• Considered a minimum standard for clinically relevant AI applications.

Data Protection and Regulation (Switzerland)

• revDSG (Revised Federal Act on Data Protection): Governs how patient data may be collected, processed, stored, and shared.

• DSFA (Data Protection Impact Assessment): Required when introducing systems that pose increased risks to patients’ rights, particularly AI tools processing sensitive health data.

• GDPR (General Data Protection Regulation): Relevant when EU-based providers, servers, or cross-border data processing are involved.