Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

• DOI: 10.3390/biomedinformatics4010047
• Publication Date: 2024-03-14T09:43:30Z
• Authors: James C. L. Chow; Valerie Wong; Kay Li
• Keywords: Computer applications to medicine. Medical informatics; R858-859.7; Neurosciences. Biological psychiatry. Neuropsychiatry; ethical AI; 03 medical and health sciences; machine learning; 0302 clinical medicine; AI; large language models; natural language processing; humanistic AI; RC321-571

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.