Applications of Large Language Models in Clinical Practice: Path, Challenges, and Future Perspectives

Abstract

Large language models (LLMs) represent a transformative advance in artificial intelligence, capable of understanding, generating and interpreting complex human language. Despite their generic design, these models have shown great potential in clinical applications, including medical diagnosis, clinical decision support, patient communication, and administrative tasks. However, deploying LLMs in clinical practice faces significant challenges, such as misinterpretation of human instructions, biased content generation, and the complexity of handling sensitive medical data. In this paper, we explore avenues for implementing LLMs in clinical settings, focusing on ethical considerations, bias mitigation, multimodal integration, simulation testing, clinical validation, and workflow integration. By addressing these challenges, we aim to improve the effectiveness, safety, and ethical standards of LLMs in healthcare, ensuring that they maximise benefits for both professionals and patients. The future outlook highlights the need for increased human-AI collaboration, improved model robustness, and adherence to ethical and regulatory standards. The ultimate goal is to provide comprehensive guidance on the effective use of LLM in clinical practice and to contribute to the development of reliable and ethical AI solutions in healthcare.

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