Preferred Label : Generative Artificial Intelligence;
MeSH definition : An artificial intelligence (AI) technique that generates synthetic artifacts by analyzing
training examples; learning their patterns and distribution; and then creating realistic
facsimiles. It uses generative modeling and advances in deep learning to produce diverse
content at scale by utilizing existing media such as text, graphics, audio, and video.
(From Association for Computing Machinery, https://doi.org/10.1145/3626110);
Définition CISMeF : “The class of AI models that emulate the structure and characteristics of input data
in order to generate derived synthetic content. This can include images, videos, audio,
text, and other digital content.” (Source: E.O. 14110) This is usually done by approximating
the statistical distribution of the input data. For example, in healthcare, generative
AI can be used to generate annotations on synthetic medical data (e.g., image features,
text labels) to help expand datasets for training algorithms.; Source: Adapted from: Meskó, B., & Topol, E. J. (2023). The imperative for regulatory
oversight of large language models (or generative AI) in healthcare. Npj Digital Medicine,
6(1). https://doi.org/10.1038/s41746-023-00873-0External Link Disclaimer Zohny, H.,
McMillan, J., & King, M. (2023). Ethics of generative AI. Journal of Medical Ethics,
49(2), 79–80. https://doi.org/10.1136/jme-2023-108909;
MeSH synonym : Artificial Intelligence, Generative;
CISMeF synonym : generative AI;
CISMeF acronym : GenAI; GAI;
MeSH hyponym : Chatbots; Chat GPT; ChatGPT; ChatGPTs;
Origin ID : D000098842;
UMLS CUI : C5940640;
Allowable qualifiers
- Automatic exact mappings (from CISMeF team)
- Currated CISMeF NLP mapping
- Record concept(s)
- Semantic type(s)
An artificial intelligence (AI) technique that generates synthetic artifacts by analyzing
training examples; learning their patterns and distribution; and then creating realistic
facsimiles. It uses generative modeling and advances in deep learning to produce diverse
content at scale by utilizing existing media such as text, graphics, audio, and video.
(From Association for Computing Machinery, https://doi.org/10.1145/3626110)
“The class of AI models that emulate the structure and characteristics of input data
in order to generate derived synthetic content. This can include images, videos, audio,
text, and other digital content.” (Source: E.O. 14110) This is usually done by approximating
the statistical distribution of the input data. For example, in healthcare, generative
AI can be used to generate annotations on synthetic medical data (e.g., image features,
text labels) to help expand datasets for training algorithms.
Source: Adapted from: Meskó, B., & Topol, E. J. (2023). The imperative for regulatory
oversight of large language models (or generative AI) in healthcare. Npj Digital Medicine,
6(1). https://doi.org/10.1038/s41746-023-00873-0External Link Disclaimer Zohny, H.,
McMillan, J., & King, M. (2023). Ethics of generative AI. Journal of Medical Ethics,
49(2), 79–80. https://doi.org/10.1136/jme-2023-108909
