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

BioMedInformatics Pub Date : 2024-03-14 DOI:10.3390/biomedinformatics4010047

J. Chow, Valerie Wong, Kay Li

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引用次数: 0

Abstract

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.

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生成式预训练转换器驱动的医疗保健对话：支持大型语言模型的医疗聊天机器人的当前趋势、挑战和未来方向

本综述通过利用自然语言处理（NLP）的对话式人工智能，探讨人工智能（AI）与医疗保健的变革性融合。本文以大型语言模型（LLMs）为重点，通过多个部分展开论述，首先概述了人工智能在医疗保健领域的意义以及对话式人工智能的作用。本文深入探讨了基本的 NLP 技术，强调了这些技术对无缝医疗对话的促进作用。本文探讨了 NLP 框架中 LLM 的演变，讨论了医疗保健中使用的关键模型，探讨了它们的优势和实施挑战。本文详细介绍了医疗保健对话中的实际应用，从以患者为中心的实用工具（如诊断和治疗建议）到医疗保健提供者支持系统。此外，还讨论了伦理和法律方面的考虑因素，包括患者隐私、伦理影响和监管合规性。综述最后强调了当前的挑战，展望了未来的趋势，并突出了 LLM 和 NLP 在重塑医疗保健互动方面的变革潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BioMedInformatics

CiteScore

1.70

自引率

0.00%

发文量