Large Language Models and ChatGPT in Medical Sciences: Foundations, Capabilities, and Challenges

Abstract

Large Language Models (LLMs) such as ChatGPT have transformed how medical knowledge is accessed and used. Built on transformer-based deep learning architectures, these systems are trained on massive datasets containing biomedical and general language sources. This training enables them to interpret clinical text, summarize research findings, and generate coherent, contextually relevant responses. In medicine, LLMs now support a wide range of applications including literature screening, clinical documentation, education, and patient interaction. ChatGPT, in particular, can help synthesize evidence, draft research manuscripts, explain complex medical concepts, and assist in decision support when appropriately guided. These tools have accelerated many aspects of medical research and practice by improving efficiency and accessibility of information. Despite these advances, challenges remain significant. LLMs can produce inaccurate or fabricated information, known as hallucinations, which poses risks in clinical and research settings. Their training data may contain biases that influence outputs, and their decision-making processes are often opaque. Ethical concerns also arise regarding authorship, accountability, and data privacy. To integrate LLMs responsibly, transparency about their use and human oversight are essential. When applied thoughtfully, these tools can enhance scientific discovery, education, and healthcare delivery while maintaining the rigor and ethical standards that underpin medical science.