Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery
34
Zitationen
6
Autoren
2023
Jahr
Abstract
Objective: The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field. Data sources: The MEDLINE database was searched on April 2023. Review methods: The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery. Results: Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients. Conclusion: While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.687 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.591 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.114 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.867 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
Institutionen
- Inserm(FR)
- Université Côte d'Azur(FR)
- Institut de Biologie Valrose(FR)
- Centre Méditerranéen de Médecine Moléculaire(FR)
- Centre Hospitalier Régional Universitaire de Brest(FR)
- Laboratoire de Traitement de l'Information Médicale(FR)
- Bedford Hospital NHS Trust(GB)
- Bedford Hospital(GB)
- Institut de Chimie de Nice(FR)
- Centre Hospitalier Universitaire de Nice(FR)