“我认为人们还没有准备好完全信任这些算法”：信任和机器学习算法在罕见病诊断中的应用。
"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.
机构信息
The Ethox Centre and Wellcome Centre for Ethics and Humanities, Nuffield Department of Population Health, and Big Data Institute, University of Oxford, Oxford, UK.
Ilumina, Cambridge, UK.
出版信息
BMC Med Ethics. 2022 Nov 16;23(1):112. doi: 10.1186/s12910-022-00842-4.
BACKGROUND
As the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning.
METHODS
Semi-structured qualitative interviews (n = 20) with stakeholders (clinical geneticists, data scientists, bioinformaticians, industry and patient support group spokespersons) who design and/or work with computational phenotyping (CP) systems. The method of constant comparison was used to analyse the interview data.
RESULTS
Interviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides-epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology's reliability and accuracy and the person using/developing them is trusted.
CONCLUSION
This study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294.
背景
随着人工智能的应用越来越广泛，计算机系统也被用于临床决策，因此需要解决对人工智能工具的信任以及其可信度问题。本文以计算表型学（CP）在发育畸形学中支持罕见病诊断为例，探讨在何种条件下我们可以信任采用机器学习的医疗人工智能工具。
方法
对设计和/或使用 CP 系统的利益攸关方（临床遗传学家、数据科学家、生物信息学家、行业和患者支持团体发言人）进行了 20 次半结构化定性访谈。采用恒比法分析访谈数据。
结果
受访者强调在识别罕见病时建立对 CP 技术使用的信任的重要性。在这些数据中，信任以两种相互关联的方式体现。首先，受访者表示，在信任关系中使用 CP 工具非常重要，患者需要信任使用 AI 工具的临床医生，而临床医生需要信任 AI 开发者，才能采用这项技术。其次，他们描述了建立对技术本身或其提供的知识的信任（认知信任）的必要性。受访者认为，如果用户能够保证技术的可靠性和准确性，并且使用/开发技术的人值得信赖，那么用于诊断罕见病的 CP 工具可能会被认为更值得信赖。
结论
本研究表明，我们需要采取深思熟虑的步骤，为医疗保健设计可靠或值得信赖的人工智能系统。此外，我们需要设计可靠或值得信赖的流程，以产生可靠的系统；这些流程可以采用随机对照试验和/或问责制、透明度和责任制的系统，以表明这些工具的认知可信度。
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