ARCHIVES

1. Miotto et al. (2018) provide a comprehensive review of how deep learning is transforming healthcare. Their work highlights key opportunities—such as improved clinical prediction and personalised care—while also outlining challenges related to data quality, interpretability, and clinical adoption.
2. Young and colleagues (2018) discuss major developments in deep learning for natural language processing. They summarise state-of-the-art models, explain how architectures like transformers have reshaped NLP, and highlight emerging trends that continue to push the field forward.
3. Esteva et al. (2019) offer an accessible guide to applying deep learning within healthcare contexts. Their article maps out practical use cases, the technical considerations involved, and future research directions for reliable medical AI systems.
4. Kannadasan et al. (2020) propose an intelligent healthcare framework that combines deep learning with NLP techniques to improve automated clinical assistance. Their study demonstrates how integrated AI components can enhance decision support and patient interaction.
5. Rani, Bawa, and Singh (2021) review the growing role of conversational agents within healthcare. They examine existing chatbot systems, discuss their clinical applications, and identify the broader challenges that must be addressed for safe deployment.
6. Rehman et al. (2022) provide an extensive survey of healthcare chatbot technology, covering architectural designs, system requirements, ongoing challenges, and anticipated future advancements in conversational AI for medical use.
7. Haleem and co-authors (2022) explore how artificial intelligence is increasingly being applied to gynaecology. Their review highlights promising opportunities for improving diagnosis and patient care, while also noting the ethical and technical obstacles that remain.
8. Fadhil and Gabrielli (2017) present an early exploration of AI- driven chatbots aimed at promoting healthy lifestyle behaviours. Their work emphasises how conversational systems can support user engagement and personalised health recommendations.
9. Jovanovic, Milinkovic, and Antic (2022) survey deep learning applications specifically focused on women’s health. The paper outlines recent progress in areas such as reproductive health, cancer detection, and maternal care, and discusses potential future innovations