Exploring the Potential of AI Language Models in Obstetrics with a Focus on Fetal Medicine: An Evaluation of the Perplexity AI Model.


Journal

Fetal diagnosis and therapy
ISSN: 1421-9964
Titre abrégé: Fetal Diagn Ther
Pays: Switzerland
ID NLM: 9107463

Informations de publication

Date de publication:
30 Nov 2023
Historique:
received: 29 06 2023
accepted: 14 11 2023
medline: 1 12 2023
pubmed: 1 12 2023
entrez: 30 11 2023
Statut: aheadofprint

Résumé

Artificial Intelligence (AI) language models, powered by natural language processing and pretrained language models, have demonstrated proficiency in natural language processing tasks, including text generation and understanding. These models can assist healthcare professionals in Fetal Medicine by providing comprehensive and accurate information, aiding in decision-making processes, and facilitating effective communication between medical practitioners and patients. However, the integration of AI language models in Fetal Medicine faces limitations and challenges. Ethical considerations, privacy concerns, and the potential for algorithmic biases are important factors to be addressed. Transparency and interpretability of AI models are critical to ensure their trustworthiness, and rigorous validation studies are required to evaluate their performance. The involvement of healthcare professionals in the development and implementation of these models is essential to maintain a patient-centered approach and to mitigate the risk of overreliance on AI systems. This article explores the potential benefits and precautions associated with the utilization of AI language models in the context of Fetal Medicine.

Identifiants

pubmed: 38035564
pii: 000535345
doi: 10.1159/000535345
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

S. Karger AG, Basel.

Auteurs

Classifications MeSH