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PMID: 38073356 Published · ppublish English Journal Article Review

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.

Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, Badnjevic A

Abstract

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

Keywords
Artificial intelligence machine learning obstetrics perinatology
MeSH 主题词
Humans Artificial Intelligence Pregnancy Obstetrics/methods,organization & administration Female Ultrasonography, Prenatal/methods Machine Learning
作者与单位
共 7 位作者,点击展开单位 / ORCID
Medjedovic Edin
Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina. | Department of Gynecology, Obstetrics and Reproductive Medicine, School of Medicine, Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina.
Stanojevic Milan
Department of Obstetrics and Gynecology, University Hospital "Sveti Duh", Zagreb, Croatia.
Jonuzovic-Prosic Sabaheta
Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina.
Ribic Emina
Public Institution Department for Health Care of Women and Maternity of Sarajevo Canton, Sarajevo, Bosnia and Herzegovina.
Begic Zijo
Department of Cardiology, Pediatric Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina.
Cerovac Anis
Department of Gynecology and Obstetrics Tesanj, General Hospital Tesanj, Bosnia and Herzegovina.
Badnjevic Almir
International Burch University, Sarajevo, Bosnia and Herzegovina. | Genetics and Bioengineering Department, Faculty of Engineering and Natural Sciences, Sarajevo, Bosnia and Herzegovina.
Article Info
Journal
Technology and health care : official journal of the European Society for Engineering and Medicine
Abbr.
Technol Health Care
ISSN
1878-7401
Published
2024-00-00
页码
1273-1287
Language
English
Country/Region
United States
NLM ID
9314590
勘误 / 撤稿关联
ExpressionOfConcernIn
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