Large Language Models in Healthcare: Opportunities and Ethical Challenges
TL;DR
A thorough review of the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations are reviewed to suggest a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices.
Abstract
The Large Language Models (LLMs) are one of the most impactful technologies in artificial intelligence, showcasing astonishing natural language understanding, medical knowledge representation, clinical decision support and healthcare communication. The incorporation of LLMs into healthcare systems can have a profound impact on enhancing diagnostic precision, patient engagement, clinical documentation, medical research, and tailored treatment planning. LLMs can be utilized to analyze vast biomedical datasets, extract key information, aid clinical decision-making, and summarize complex medical records, due to their ability to process large amounts of information and understand complex patterns using advanced deep learning architectures. However, the use of LLMs in healthcare brings significant ethical, legal, technical and regulatory issues. Many challenges still stand in the way of large-scale implementation, such as patient privacy concerns, data security, algorithmic bias, explainability, misinformation, accountability, transparency, and regulatory compliance. In addition, there is a need for careful evaluation and ongoing monitoring of the performance of the models to guarantee fairness and reliability for different patient groups. The present paper is a thorough review of the opportunities and ethical concerns related to LLM in healthcare. It reviews the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations. In addition, the paper suggests a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices. The results offer insights that can guide and inform researchers, healthcare practitioners, policy makers, and AI developers in the design and development of trustworthy, transparent, and human-centered intelligent healthcare systems.