Artificial intelligence (AI) is a powerful tool for several healthcare tasks. AI tools are suited to optimize predictive models in medicine. Ethical debates about AI’s extension of the predictive power of medical models suggest a need to adapt core principles of medical ethics. This article demonstrates that a popular interpretation of the principle of justice in healthcare needs amendment given the effect of AI on decision-making. The procedural approach to justice, exemplified with Norman Daniels and James Sabin’s accountability for reasonableness (...) conception, needs amendment because, as research into algorithmic fairness shows, it is insufficiently sensitive to differential effects of seemingly just principles on different groups of people. The same line of research generates methods to quantify differential effects and make them amenable for correction. Thus, what is needed to improve the principle of justice is a combination of procedures for selecting just criteria and principles and the use of algorithmic tools to measure the real impact these criteria and principles have. In this article, the author shows that algorithmic tools do not merely raise issues of justice but can also be used in their mitigation by informing us about the real effects certain distributional principles and criteria would create. (shrink)

Tensions between respect for autonomy and paternalism loom large in Ferrario et al ’s discussion of artificial intelligence (AI)-based preference predictors.1 To be sure, their analysis (rightfully) brings out the moral matter of respecting patient preferences. My point here, however, is that their consideration of AI-based preference predictors in treatment of incapacitated patients opens more fundamental moral questions about the desirability of over-ruling considered patient preferences, not only if these are disclosed by surrogates, but possibly also in treating competent patients. (...) I do not advocate such an evolution—the moral desirability of that calls for a much broader debate, one in which the meaning of ‘doing good’ in medicine, and how this intersects with normative views on ‘the goal(s) of medicine’ would be central elements. While my aim in this piece is more modest, I nonetheless hope to approach it sideways, by indicating how the contribution by Ferrario et al reopens discussion about paternalism and the normativity of preferences in medicine. This follows from what these authors give as reason for endorsing this technology in care for incapacitated patients. Their argument for employing such tools in case of incapacitation is based on the premise that advice of surrogate decision-makers about care preferences is suboptimal because of their biased perception …. (shrink)

In this paper, we address the question of whether AI should be used for suicide prevention on social media data. We focus on algorithms that can identify persons with suicidal ideation based on their postings on social media platforms and investigate whether private companies like Facebook are justified in using these. To find out if that is the case, we start with providing two examples for AI-based means of suicide prevention in social media. Subsequently, we frame suicide prevention as an (...) issue of beneficence, develop two fictional cases to explore the scope of the principle of beneficence and apply the lessons learned to Facebook’s employment of AI for suicide prevention. We show that Facebook is neither acting under an obligation of beneficence nor acting meritoriously. This insight leads us to the general question of who is entitled to help. We conclude that private companies like Facebook can play an important role in suicide prevention, if they comply with specific rules which we derive from beneficence and autonomy as core principles of biomedical ethics. At the same time, public bodies have an obligation to create appropriate framework conditions for AI-based tools of suicide prevention. As an outlook we depict how cooperation between public and private institutions can make an important contribution to combating suicide and, in this way, put the principle of beneficence into practice. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Laacke et al. describe the emergence of novel analytical tools—artificial intelligence depression detectors —that employ artificial intelligence to predict depression. The authors the...

A mark of our modern age is the translation of the non-digital to the digital, an evolution likely only to accelerate and to demand the proactive development of robust ethical guidance to navigate...

Laacke et al. investigate the ethical implications of possible artificial intelligence systems that automatically detect signs of depression by analyzing data from social media. The art...

Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the “empirical turn” in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased (...) use of empirical methodologies from social sciences, the proliferation of health-related subtypes of technology ethics might have a comparable impact on current bioethical research. This systematic journal review analyses the reporting of ethical frameworks and non-empirical methods in argument-based research articles on digital technologies in medicine, health care and public health that have been published in high-impact bioethics journals. We focus on articles reporting non-empirical research in original contributions. Our aim is to describe currently used methods for the ethical analysis of ethical issues regarding the application of digital technologies in medicine, health care and public health. We confine our analysis to non-empirical methods because empirical methods have been well-researched elsewhere. Finally, we discuss our findings against the background of established methods for health technology assessment, the lack of a typology for non-empirical methods as well as conceptual and methodical change in bioethics. Our descriptive results may serve as a starting point for reflecting on whether current ethical frameworks and non-empirical methods are appropriate to research ethical issues deriving from the application of digital technologies in medicine, health care and public health. (shrink)

In “Artificial Intelligence, Social Media and Depression,” Laacke and colleagues consider the ethical implications of artificial intelligence depression detector tools to assist pract...

The target article by Laacke et al. focuses on the specific context of identifying people in social media with a high risk of depression by using artificial intelligence technologies. I...

Digital mental health can be understood as the in situ quantification of an individual’s data from personal devices to measure human behavior in both health and disease (Huckvale, Venkatesh and Chr...

The impact of machine learning in healthcare on patient informed consent is now the subject of significant inquiry in bioethics. However, the topic has predominantly been considered in the context of black box diagnostic or treatment recommendation algorithms. The impact of machine learning involved in healthcare resource allocation on patient consent remains undertheorized. This paper will establish where patient consent is relevant in healthcare resource allocation, before exploring the impact on informed consent from the introduction of black box machine learning (...) into resource allocation. It will then consider the arguments for informing patients about the use of machine learning in resource allocation, before exploring the challenge of whether individual patients could principally contest algorithmic prioritization decisions involving black box machine learning. Finally, this paper will examine how different forms of opacity in machine learning involved in resource allocation could be a barrier to patient consent to clinical decision-making in different healthcare contexts. (shrink)

BackgroundMachine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory (...) as an analytical lens for investigating how medical action at the micro level and the physician–patient relationship might be affected by the employment of ML_CDSS.Main textProfessionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.ConclusionsUsing ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making. (shrink)

The rapid expansion of computational tools and of data science methods in healthcare has, undoubtedly, raised a whole new set of bioethical challenges. As Laacke and colleagues rightly note,...

Online exam supervision technologies have recently generated significant controversy and concern. Their use is now booming due to growing demand for online courses and for off-campus assessment options amid COVID-19 lockdowns. Online proctoring technologies purport to effectively oversee students sitting online exams by using artificial intelligence systems supplemented by human invigilators. Such technologies have alarmed some students who see them as a “Big Brother-like” threat to liberty and privacy, and as potentially unfair and discriminatory. However, some universities and educators defend (...) their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This essay provides one of the first sustained moral philosophical analyses of these technologies, focusing on ethical notions of academic integrity, fairness, non-maleficence, transparency, privacy, autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics, and all are relevant to education. The essay discusses these ethical issues. It also offers suggestions for educational institutions and educators interested in the technologies about the kinds of inquiries they need to make and the governance and review processes they might need to adopt to justify and remain accountable for using online proctoring technologies. The rapid and contentious rise of proctoring software provides a fruitful ethical case study of how AI is infiltrating all areas of life. The social impacts and moral consequences of this digital technology warrant ongoing scrutiny and study. (shrink)

The authors of the concept Health-Related Digital Autonomy have laid the first building block to examine the interactions between artificial intelligence, social media, and depression f...

Trust and trustworthiness are essential for good healthcare, especially in mental healthcare. New technologies, such as mobile health apps, can affect trust relationships. In mental health, some apps need the trust of their users for therapeutic efficacy and explicitly ask for it, for example, through an avatar. Suppose an artificial character in an app delivers healthcare. In that case, the following questions arise: Whom does the user direct their trust to? Whether and when can an avatar be considered trustworthy? Our (...) study aims to analyze different dimensions of trustworthiness in the context of mobile health app use. We integrate O'Neill's account of autonomy, trust, and trustworthiness into a model of trustworthiness as a relational concept with four relata: B is trustworthy with respect to A regarding the performance of Z because of C. Together with O'Neill's criteria of trustworthiness (honesty, competence, and reliability), this four-sided model is used to analyze different dimensions of trustworthiness in an exemplary case of mobile health app use. Our example focuses on an app that uses an avatar and is intended to treat sleep difficulties. The conceptual analysis shows that interpreting trust and trustworthiness in health app use is multi-layered and involves a net of interwoven universal obligations. At the same time, O'Neill's approach to autonomy, trust, and trustworthiness offers a normative account to structure and analyze these complex relations of trust and trustworthiness using mobile health apps. (shrink)

Recent medical innovations like ‘omics’ technologies, mobile health (mHealth) applications or telemedicine are perceived as part of a shift towards a more preventive, participatory and affordable healthcare model. These innovations are often regarded as ‘disruptive technologies’. It is a topic of debate to what extent these technologies may transform the medical enterprise, and relatedly, what this means for medical ethics. The question of whether these developments disrupt established ethical principles like respect for autonomy has indeed received increasing normative attention during (...) the past years. Yet, more fundamental ethical considerations of a possible disruption of the concept of ‘medical necessity’ (and the potential ensuing clinical ramifications) have been limited if not absent. It is our modest – though morally and practically relevant – aim to address this objective in this article, by exploring how the current wave of allegedly disruptive innovation is invoking a resurgence of this concept and related debates on ‘the’ goal(s) of medicine. (shrink)

In “Artificial Intelligence, Social Media and Depression: A New Concept of Health-Related Digital Autonomy,” Laacke and colleagues posit a revised model of autonomy when using digital algori...

The COVID-19 pandemic has been a threat to both physical and mental health. The spreading disease and its impacts, the containment measures and the way all of our lives have dramatically changed ha...

Artificial Intelligence systems—and in particular various types of machine learning models—have significant potential for improving the performance and effectiveness of diagnostics and treatme...

Laacke et al. argue that an extended concept of patient autonomy—Health-Related Digital Autonomy —is required to address the autonomy-related ethical challenges associated with the pot...

Neurodevices that collect neural (or brain activity) data have been characterised as having the ability to register the inner workings of human mentality. There are concerns that the proliferation of such devices in the consumer-directed realm may result in the mass processing and commercialisation of neural data (as has been the case with social media data) and even threaten the mental privacy of individuals. To prevent this, some argue that all raw neural data should be conceptualised and regulated as “medical (...) data” even if it is collected from consumer-directed devices in obviously non-clinical settings. In this paper, we argue that without a clearer formulation of what does and does not count as medical data, this approach might also uncritically enlarge the scope for medical influence and an unwarranted medicalisation of everyday aspects of mental life. Indeed, if we were to accept the position that all neural data is medical data because it offers insights into personally sensitive information (such a person’s thoughts, emotions, or intentions) then this could even unnecessarily expand the boundaries of medical data to other forms of data that otherwise seem to be non-clinical. If all brain data (neural activity data) is considered to be medical data even when collected from consumer-directed devices then we might unintentionally reduce meaningful distinctions between what is and what is not rightfully in the purview of medicine. (shrink)