@article{https://doi.org/10.1002/eng2.70365,
author = {Salehin, Imrus and Tomal Ahmed Sajib, Md and Huda Badhon, Nazmul and Sakibul Hassan Rifat, Md and Amin, Nazrul and Nessa Moon, Nazmun},
title = {Systematic Literature Review of LLM-Large Language Model in Medical: Digital Health, Technology and Applications},
journal = {Engineering Reports},
volume = {7},
number = {9},
pages = {e70365},
keywords = {AI, Health, LLM, Medical AI, NLP},
doi = {https://doi.org/10.1002/eng2.70365},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/eng2.70365},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/eng2.70365},
abstract = {ABSTRACT Large language models (LLMs), like the GPT series, have recently emerged as transformative tools in the medical field due to their human-like language generation and understanding. This systematic review examines the evolution, applications, and challenges of medical LLMs in digital health and clinical technology. A structured search was conducted across ScienceDirect, PubMed, Scopus, and manual sources from 2007 to 2025, following PRISMA 2020 guidelines. After applying inclusion and exclusion criteria, 185 studies were selected from an initial pool of 698 papers. Among these, 30 representative studies were analyzed in-depth based on their relevance, methodological quality, and contribution to diverse LLM applications in health care. Most research centered on GPT-based models, with over 81\% demonstrating strong performance in language generation, diagnostic assistance, and clinical documentation, based on automated metrics and human feedback. Notably, some models achieved up to 90\% satisfaction from healthcare professionals. The findings reveal LLM's potential to enhance patient interaction, decision support, and overall healthcare efficiency. This review contributes by synthesizing key advancements, assessing model performance, and outlining ethical challenges such as trust, privacy, and safe deployment. It offers novel insights for researchers and practitioners seeking to adopt or improve LLM integration in health care. Future directions include improving transparency, developing domain-specific models, and establishing regulatory frameworks for responsible use.},
year = {2025}
}