Vol. 2 No. 1 (2026): Journal of Computing and Engineering - JCE

Can ChatGPT answer Pregnant Women Questions?

Aseel Abdulameer Mohammed¹

¹College of Medicine, University of Kufa, Aseela.albubaqer@uokufa.edu.iq, ORCID 0009-0002-9389-0868

OpenAI has developed ChatGPT which is considered as one of the famous artificial intelligence tools. It provides a significant advancement and particularly noted in order to create a speaking conversations with human. This review paper shows the use of ChatGPT in women care in their pregnancy time. ChatGPT provides available and sympathetic information and replies to any questions about childbirth, pregnancy and woman care. The reliability and accuracy of ChatGPT’s answers is important to be checked.  It also helps to support decision making in pregnancy clinical problems but it recently lacks the precision and appropriate answers without doctor oversight. Moreover, it provides technical accurate information, proper medical answers with lacks of references.  However, it is not always giving correct answers.  very few studies have been done on this area and most of them in English. This research attented to ask ChatGPT in Arabic. We choose five of the common questions that always be asked by pregnant ladies in their booking visit and compare the answers of ChatGPT with the answers of three famous obstetrician doctors in Iraq in order to see the similarities or differences.

Keywords: Artificial intelligence, AI, pregnancy questions, pregnant woman, early pregnancy.

Artificial Intelligence in Big Data: A Literature Review

Eman Yahya Maarof¹

¹Facility of Computer Studies, Arab Open University, Saudi Arabia, e.maarof@arabou.edu.sa, ORCID 0009-0003-8456-9826

This Artificial Intelligence (AI) and Big Data combination has revolutionized data-driven decision-making in almost all the scientific and industrial fields. Over the last six years, from 2020 to 2026, the field witnessed an unprecedented degree of change, as deep learning became more mature, transformer architectures and large language models emerged and became more popular, federated learning and edge learning began to gain farming, and privacy and algorithmic accountability became stricter. This paper is a systematic review of the literature in Scopus and Web of Science following PRISMA 2020 guidelines. After deduping records, two stage screening and quality checking with Kitchenham & Charters criteria, 138 Primary studies were selected from a pool of 4182 records. The objective of this research is to bring together the evidence for the four research questions. Results indicate the deep-learning family continues to be the top applied family (46.4% of studies), but the field is on a structural shift with the transformer- and LLM-based approaches rising from less than 3.0% in 2020, to more than 28% in 2025, and with federated-learning slightly more than 9% jumping to roughly tripled in the same timeframe. Application areas and domains are still largely dominated by the areas of healthcare, IoT/smart cities, and finance, while scalability, privacy, explainability, data quality, and energy efficiency remain among the most frequently cited open problems. The paper ends with a research agenda consisting of six points, most of them focusing on trustworthy, sustainable, and human-centric AI for Big Data.

Keywords: Artificial Intelligence, Big Data, Deep learning, Federated learning, Large language models, Edge AI, Literature review.