Large Language Models (LLMs) have emerged as breakthrough AI tools for advanced text comprehension and generation. With rapid improvements in their capabilities, the applications of LLMs across different industries are now increasing with a lot of interest. In the healthcare sector, using LLMs will revolutionize clinical decision support, medical research, patient communication, and administrative workflows, thereby making healthcare delivery more efficient and accessible. The impact and present applications of LLMs in healthcare contexts are assessed in this systematic review including key implementation areas, with assessments of their effectiveness toward improving healthcare outcomes and relevant challenges and ethical considerations. A systematic search following the PRISMA guidelines was conducted in PubMed, ACM Digital Library and IEEE. After duplicates were removed, titles and abstracts were screened, and full-text articles were examined against predetermined inclusion and exclusion criteria. This systematic review includes 34 peer-reviewed journal articles, published in English, between 2020–2024, excluding conference papers, review papers, and case study papers. Key trends reveal a dramatic increase of LLM-related healthcare research after 2022, with high-impact applications in clinical administration, medical research, education, and patient care. This review explores the application of LLMs in different healthcare fields such as cancer diagnostics, dentistry, nephrology, and dermatology. It will also indicate their capabilities to improve diagnosis accuracy, predict disease progression, and enhance clinical decision-making. Nevertheless, the increasing use of LLMs in healthcare evokes serious ethical issues about data privacy, algorithmic bias, and potential misuse.