Prospectives and drawbacks of ChatGPT in healthcare and clinical medicine

Khadija Alam, Akhil Kumar, F. N. U. Samiullah
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Abstract

The large language model (LLM) ChatGPT-3.5, a member of generative pre-training transformer (GPT) models created by artificial intelligence (AI), is an updated and finely tuned version of previously launched AI chatbots. It is trained on a large volume of text data available on the internet, can produce human-like responses to a range of prompts and inquiries, and interprets and conversationally creates text, making it suitable for participating in interactive human conversations on a range of topics. Since its release in November 2022, ChatGPT has gained quick popularity not only among the general population but also among healthcare workers and researchers in all fields owing to its versatile, potent, and reliable benefits regarding scientific writing and medical education. Consequently, many studies have been done on ChatGPT regarding scientific research and medical education that have greatly enlightened medical professionals about its efficient use and probable risks. However, its effectiveness in healthcare and clinical medicine is still being determined, given its criticism regarding the authenticity of its diagnostic decisions. This review aims to highlight its viable usage in clinical practice, such as virtual assistance and patient communication, and the potential associated drawbacks, including a lack of human judgment and interactivity, data quality, accountability, and transparency, the risk of overreliance, and medico-legal and ethical considerations.

ChatGPT 在医疗保健和临床医学中的前景和弊端
大型语言模型(LLM) ChatGPT-3.5是人工智能(AI)创建的生成式预训练转换器(GPT)模型的一员,是先前推出的AI聊天机器人的更新和微调版本。它在互联网上大量可用的文本数据上进行训练,可以对一系列提示和查询产生类似人类的响应,并且可以解释和对话地创建文本,使其适合参与关于一系列主题的交互式人类对话。自2022年11月发布以来,ChatGPT不仅在普通人群中,而且在所有领域的医疗工作者和研究人员中迅速流行起来,因为它在科学写作和医学教育方面具有多功能,强大和可靠的好处。因此,在科学研究和医学教育方面对ChatGPT进行了许多研究,使医疗专业人员对其有效使用和可能存在的风险有了很大的了解。然而,鉴于其诊断决定的真实性受到批评,其在医疗保健和临床医学中的有效性仍有待确定。本综述旨在强调其在临床实践中的可行应用,例如虚拟辅助和患者沟通,以及潜在的相关缺点,包括缺乏人类判断和交互性,数据质量,问责制和透明度,过度依赖的风险,以及医学法律和伦理考虑。
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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