ChatGPT会改变医疗保健吗？| Nature Medicine

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ChatGPT and other large language models may be able to enhance healthcare delivery and patients’ quality of life. But they will need to be tailored to specific clinical needs first. ChatGPT和其他大型语言模型可能能够提高医疗保健的交付和患者的生活质量。但它们首先需要根据特定的临床需求进行定制。

Large language models, such as ChatGPT, use deep learning (DL) to reproduce human language in a convincing and human-like way. They are becoming increasingly common and are already being used in content marketing, customer services and a variety of business applications. As a result, it is inevitable that language models will also soon debut in healthcare, an area where they hold tremendous potential to improve health and enhance patients’ lives, but not without pitfalls. 大型语言模型，如ChatGPT，使用深度学习（DL）以令人信服和类似人类的方式重现人类语言。它们正变得越来越普遍，并已被用于内容营销、客户服务和各种业务应用程序。因此，语言模型也将不可避免地很快在医疗保健领域首次亮相，这是一个它们在改善健康和提高患者生活方面拥有巨大潜力的领域，但也并非没有陷阱。

ChatGPT’s ability to engage people with human-like conversation serves as a reminder of how important language and communication are to the human experience and well-being. Effective communication through language helps people to forge relationships with others, including the relationships between patients and healthcare professionals. One way that language models could improve care is by learning and producing language to assist patients in communicating with healthcare workers and with each other. For instance, it could help to improve compliance with medical prescriptions, by making the language more accessible to the patient and by reducing the chance of miscommunication. In addition, given that the quality of patient–physician relationships affect patient outcomes in a variety of conditions ranging from mental health1 to obesity2 and cancer3, it is reasonable to assume that using language models to strengthen those relationships through better communication would have beneficial impact for patients. ChatGPT能够让人们进行类似人类的对话，这提醒人们语言和沟通对人类体验和福祉的重要性。通过语言进行有效沟通有助于人们与他人建立关系，包括患者与医疗保健专业人员之间的关系。语言模型可以改善护理的一种方式是通过学习和产生语言来帮助患者与医护人员以及彼此进行沟通。例如，它可以通过使语言更容易被病人理解和减少误解的机会，帮助改善对医疗处方的遵守。此外，考虑到患者-医生关系的质量会影响从精神健康 1 到肥胖 2 和癌症 3 的各种情况下的患者结果，可以合理地假设使用语言模型通过更好的沟通来加强这些关系将对患者产生有益的影响。

Large language models could also help with health interventions that rely on communication between non-professional peers. In a recent study, a language model4 that was trained to rewrite text in a more empathic way made communication easier in a peer-to-peer mental health support system, which enhanced non-expert conversational abilities. This example highlights the potential of using human–artificial intelligence collaboration to improve a variety of community-based health tasks that rely on peer- or self-administered therapy, such as in cognitive behavioral therapy. Against a background of limited healthcare resources coupled with a growing mental health crisis — as reported5 by the US Centers for Disease Control and Prevention — application of such tools could increase assistance coverage, especially in settings that bypass the need for delivery of care by specialized healthcare workers. 大型语言模型还可以帮助依赖于非专业同行之间沟通的健康干预。在最近的一项研究中，一种语言模型 4 被训练成以更移情的方式重写文本，使点对点心理健康支持系统中的沟通变得更容易，这增强了非专家的对话能力。这个例子强调了使用人工智能协作来改善各种基于社区的健康任务的潜力，这些任务依赖于同伴或自我管理的治疗，例如认知行为治疗。在有限的医疗资源加上日益严重的心理健康危机的背景下-正如美国疾病控制和预防中心报告的 5 -应用这些工具可以增加援助覆盖面，特别是在绕过专业医疗工作者提供护理的情况下。

Language communication can be both a therapeutic intervention, as in psychotherapy, and the target of therapy, such as in speech impairments such as aphasia. There are various types of language impairment, with different causes and coexisting conditions. Language models could be useful tools for personalized medicine approaches. For example, patients with neurodegenerative conditions may lose their ability to communicate through spoken language and progressively lose their vocabulary, which can worsen their social isolation and accelerate the degenerative process. Given that individual patients often present with unique combinations of specific patterns of neurodegenerative phenotypes, they may benefit from personalized approaches facilitated by artificial intelligence. Language models could also help patients with neurodegenerative diseases to expand their vocabulary or comprehend information more easily. They can achieve this by complementing language with other media or by reducing the complexity of the input the patients receive. In each of these cases, the algorithm would be specifically tailored to needs of each individual patient. These models could also play an important part in the development of speech brain–computer interfaces, which are designed to decode brain signals and imagined speech into vocalized language in people with aphasia. Such technologies not only would enhance coherence, but also reproduce the patient’s communication style and meaning more accurately. 语言交流既可以是一种治疗性干预，如在心理治疗中，也可以是治疗的目标，如在失语症等言语障碍中。语言障碍的类型多种多样，成因各异，并存的情况也不尽相同。语言模型可能是个性化医疗方法的有用工具。例如，患有神经退行性疾病的患者可能会失去通过口语进行交流的能力，并逐渐失去词汇量，这可能会加剧他们的社会孤立并加速退行性过程。鉴于个体患者通常表现出神经退行性表型的特定模式的独特组合，他们可能会受益于人工智能促进的个性化方法。语言模型还可以帮助神经退行性疾病患者扩大词汇量或更容易地理解信息。他们可以通过用其他媒体补充语言或减少患者接收的输入的复杂性来实现这一点。在这些情况中的每一种情况下，算法将针对每个个体患者的需求进行专门定制。这些模型也可以在语音脑机接口的开发中发挥重要作用，该接口旨在将大脑信号和想象的语音解码为失语症患者的发声语言。这种技术不仅可以增强连贯性，而且可以更准确地再现患者的沟通风格和含义。

While their potential is huge, most applications of DL-based language models in healthcare are not yet ready for primetime. Specific clinical applications of DL-based language models will require extensive training on expert annotations in order to achieve acceptable standards of clinical performance and reproducibility. Early attempts6 at using these models as clinical diagnostic tools without additional training have shown limited success, with the algorithm performance remaining lower than that of practicing physicians. Therefore, while it is tempting to bypass this very expensive requirement by relying on large training datasets and the adaptive learning capabilities of these tools, the evidence accumulated so far highlights the need for extensive and formal evaluation of language models against standard clinical practices after they have been trained for specific clinical tasks, such as diagnostic advice and triaging. 虽然它们的潜力巨大，但基于DL的语言模型在医疗保健领域的大多数应用还没有准备好。基于DL的语言模型的具体临床应用将需要对专家注释进行广泛的培训，以达到临床性能和可重复性的可接受标准。在没有额外培训的情况下使用这些模型作为临床诊断工具的早期尝试 6 已经显示出有限的成功，算法性能仍然低于执业医师的性能。因此，虽然通过依赖于大型训练数据集和这些工具的自适应学习功能来绕过这个非常昂贵的要求是很有吸引力的，但迄今为止积累的证据强调了在针对特定临床任务（例如诊断建议和分类）进行训练后，需要针对标准临床实践对语言模型进行广泛和正式的评估。

Using ChatGPT or other advanced conversational models as sources of medical advice by the public should be also a source of concern. Part of the allure of these new tools stems from humans being innately drawn toward anthropomorphic entities. People tend to more naturally trust something that mimics human behaviors and responses, such as the responses generated by ChatGPT. Consequently, people could be tempted to use conversational models for applications for which they were not designed, and in lieu of professional medical advice, such as retrieving possible diagnoses from a list of symptoms or deriving treatment recommendations. Indeed, a survey7 reported that around one-third of the US adults sought medical advice on the internet for self-diagnoses, with only around half of these respondents subsequently consulting a physician about the web-based results. 公众使用ChatGPT或其他高级会话模型作为医疗建议的来源也应该引起关注。这些新工具的吸引力部分源于人类天生就被拟人化的实体所吸引。人们倾向于更自然地信任模仿人类行为和反应的东西，例如ChatGPT生成的反应。因此，人们可能会倾向于将会话模型用于其未设计的应用程序，并代替专业的医疗建议，例如从症状列表中检索可能的诊断或导出治疗建议。事实上，一项调查 7 报告说，大约三分之一的美国成年人在互联网上寻求医疗建议进行自我诊断，只有大约一半的受访者随后向医生咨询基于网络的结果。

This means that the use of ChatGPT and other language models in healthcare will require careful consideration to ensure that safeguards are in place to protect against potentially dangerous uses, such as bypassing expert medical advice. One such protection measure could be as simple as an automated warning that is triggered by queries about medical advice or terms to remind users that the model outputs do not constitute or replace expert clinical consultation. It is also important to note that these technologies are evolving at a much faster pace than the regulators, government and advocates can cope. Given their wide availability, and potential societal impact, it is critical that all stakeholders — developers, scientists, ethicists, healthcare professionals, providers, patients, advocates, regulators and governmental agencies — get involved and are engaged in identifying the best way forward. Within a constructive and alert regulatory environment, DL-based language models could have a transformative impact in healthcare, augmenting rather than replacing human expertise, and ultimately improving quality of life for many patients. 这意味着在医疗保健中使用ChatGPT和其他语言模型需要仔细考虑，以确保采取保护措施，以防止潜在的危险用途，例如绕过专家的医疗建议。一种这样的保护措施可以是简单的自动警告，其由关于医疗建议或术语的查询触发，以提醒用户模型输出不构成或取代专家临床咨询。同样重要的是要注意到，这些技术的发展速度远远超过了监管机构、政府和倡导者所能科普的速度。鉴于其广泛的可用性和潜在的社会影响，所有利益相关者-开发人员，科学家，伦理学家，医疗保健专业人员，提供者，患者，倡导者，监管机构和政府机构-参与并参与确定最佳前进方向至关重要。在一个建设性和警惕的监管环境中，基于DL的语言模型可能会对医疗保健产生变革性的影响，增强而不是取代人类的专业知识，并最终改善许多患者的生活质量。

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