Vellido Alfredo
Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya (UPC BarcelonaTech), Barcelona, Spain.
Kidney Dis (Basel). 2019 Feb;5(1):11-17. doi: 10.1159/000492428. Epub 2018 Sep 3.
Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the "2nd Meeting of Science and Dialysis: Artificial Intelligence," organized in the Bellvitge University Hospital, Barcelona, Spain.
AI and ML are attracting much interest from the medical community as key approaches to knowledge extraction from data. These approaches are increasingly colonizing ambits of social impact, such as medicine and healthcare. Issues of social relevance with an impact on medicine and healthcare include (although they are not limited to) fairness, explainability, privacy, ethics and legislation.
医学正日益成为一个以数据为中心的学科，除了传统的统计方法外，人工智能（AI），尤其是机器学习（ML），在医学数据分析中引起了广泛关注。有人认为，人工智能正在经历一个快速的商品化过程。这种描述正确地反映了当前人工智能的发展进程及其对社会的影响。因此，与人工智能和机器学习使用相关的社会问题不应再被忽视，在医学领域更是如此。这些社会问题可能有多种形式，但它们都需要从以人为本的角度设计模型，纳入与人类相关的要求和限制。在这篇简短的论文中，我们讨论了一些影响人工智能和机器学习在医学中应用的具体问题，如公平性、隐私和匿名性、可解释性和可解读性，以及一些更广泛的社会问题，如伦理和立法。我们认为，所有这些都是需要考虑的相关方面，以便实现促进对基于人工智能和机器学习的技术的接受这一目标，并遵守有关数字技术对伦理和隐私敏感问题影响的不断演变的立法。我们在此的具体目标是思考所有这些主题如何影响人工智能和机器学习的医学应用。本文包含了在西班牙巴塞罗那贝尔维奇大学医院举办的“第二届科学与透析：人工智能会议”的部分内容。
人工智能和机器学习作为从数据中提取知识的关键方法，正引起医学界的广泛关注。这些方法正日益渗透到社会影响领域，如医学和医疗保健。对医学和医疗保健有影响的社会相关问题包括（但不限于）公平性、可解释性、隐私、伦理和立法。
