Systems medicine, which is based on computational modelling of biological systems, is emerging as an increasingly prominent part of the personalized medicine movement. It is often promoted as ‘P4 medicine’. In this article, we test promises made by some of its proponents that systems medicine will be able to develop a scientific, quantitative metric for wellness that will eliminate the purported vagueness, ambiguity, and incompleteness—that is, normativity—of previous health definitions. We do so by examining the most concrete and relevant evidence (...) for such a metric available: a patent that describes a systems medicine method for assessing health and disease. We find that although systems medicine is promoted as heralding an era of transformative scientific objectivity, its definition of health seems at present still normatively based. As such, we argue that it will be open to influence from various stakeholders and that its purported objectivity may conceal important scientific, philosophical, and political issues. We also argue that this is an example of a general trend within biomedicine to create overly hopeful visions and expectations for the future. (shrink)

Precision Medicine has become a common label for data-intensive and patient-driven biomedical research. Its intended future is reflected in endeavours such as the Precision Medicine Initiative in the USA. This article addresses the question whether it is possible to discern a new ‘medical cosmology’ in Precision Medicine, a concept that was developed by Nicholas Jewson to describe comprehensive transformations involving various dimensions of biomedical knowledge and practice, such as vocabularies, the roles of patients and physicians and the conceptualisation of disease. (...) Subsequently, I will elaborate my assessment of the features of Precision Medicine with the help of Michel Foucault, by exploring how precision medicine involves a transformation along three axes: the axis of biomedical knowledge, of biomedical power and of the patient as a self. Patients are encouraged to become the managers of their own health status, while the medical domain is reframed as a data-sharing community, characterised by changing power relationships between providers and patients, producers and consumers. While the emerging Precision Medicine cosmology may surpass existing knowledge frameworks; it obscures previous traditions and reduces research-subjects to mere data. This in turn, means that the individual is both subjected to the neoliberal demand to share personal information, and at the same time has acquired the positive ‘right’ to become a member of the data-sharing community. The subject has to constantly negotiate the meaning of his or her data, which can either enable self-expression, or function as a commanding Superego. (shrink)

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its (...) emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences. (shrink)

Evidence-based medicine and medical professionalism are two prominent notions in current medical debates. However, proponents of professionalism fear a restriction in doctors’ freedom to make their best decisions for individual patients caused by the influence of EBM and highly standardised decision procedures. The challenge which EBM allegedly poses to physicians’ discretion forms the starting point for an analysis of the relationship between professionalism, as an inherent value system of medical practice, and EBM, as an approach to optimise the decision-making for (...) individual patients. The analysis starts with a brief conceptual clarification of the ambiguous term “professionalism”. It then focuses on three key aspects of medical professionalism which may come into conflict with the basic tenets of EBM. The potential tensions between professional autonomy and clinical practice guidelines, individualised care and standardisation, and esoteric authority and public accountability are analysed and a suggestion for reconcilement regarding each point is made. The article closes with a summary on how a better reflection on medical professionalism may help towards a fuller understanding of EBM and vice versa. (shrink)

What position on dualism does medicine require? Our understanding of that ques- tion has been dictated by holism, as defined by the biopsychosocial model, since the late twentieth century. Unfortunately, holism was characterized at the start with con- fused definitions of ‘dualism’ and ‘reductionism’, and that problem has led to a deep, unrecognized conceptual split in the medical professions. Some insist that holism is a nonreductionist approach that aligns with some form of dualism, while others insist it’s a reductionist view (...) that sets out to eradicate dualism. It’s important to consider each version. Nonreductive holism is philosophically consistent and clinically unprob- lematic. Reductive holism, however, is conceptually incoherent—yet it is the basis for the common idea that the boundary between medical and mental health disorders must be vague. When we trace that idea through to its implementation in medical practice, we find evidence that it compromises the safety of patient care in the large portion of cases where clinicians grapple with diagnosis at the boundary between psychiatry and medicine. Having established that medicine must embrace some form of nonreduc- tionism, I argue that Chalmers’ naturalistic dualism is a stronger prima facie candidate than the nonreductive alternatives. Regardless of which form of nonreductionism we prefer, some philosophical corrections are needed to give medicine a safe and coherent foundation. (shrink)

Trust relations in the health services have changed from asymmetrical paternalism to symmetrical autonomy-based participation, according to a common account. The promises of personalized medicine emphasizing empowerment of the individual through active participation in managing her health, disease and well-being, is characteristic of symmetrical trust. In the influential Kantian account of autonomy, active participation in management of own health is not only an opportunity, but an obligation. Personalized medicine is made possible by the digitalization of medicine with an ensuing increased (...) tailoring of diagnostics, treatment and prevention to the individual. The ideal is to increase wellness by minimizing the layer of interpretation and translation between relevant health information and the patient or user. Arguably, this opens for a new level of autonomy through increased participation in treatment and prevention, and by that, increased empowerment of the individual. However, the empirical realities reveal a more complicated landscape disturbed by information ‘noise’ and involving a number of complementary areas of expertise and technologies, hiding the source and logic of data interpretation. This has lead to calls for a return to a mild form of paternalism, allowing expertise coaching of patients and even withholding information, with patients escaping responsibility through blind or lazy trust. This is morally unacceptable, according to Kant’s ideal of enlightenment, as we have a duty to take responsibility by trusting others reflexively, even as patients. Realizing the promises of personalized medicine requires a system of institutional controls of information and diagnostics, accessible for non-specialists, supported by medical expertise that can function as the accountable gate-keeper taking moral responsibility required for an active, reflexive trust. (shrink)

Big data techniques, data-driven science and their technological applications raise many serious ethical questions, notably about privacy protection. In this paper, we highlight an entanglement between epistemology and ethics of big data. Discussing the mobilisation of big data in the fields of biomedical research and health care, we show how an overestimation of big data epistemic power – of their objectivity or rationality understood through the lens of neutrality – can become ethically threatening. Highlighting the irreducible non-neutrality at play in (...) big data tools, we insist upon the ethical importance of a critical epistemological approach in which big data are understood as possibly valuable only when coupled with human intelligence and evaluative rationality. (shrink)

Is medicalization always harmful? When does medicine overstep its proper boundaries? The aim of this article is to outline the pragmatic criteria for distinguishing between medicalization and over-medicalization. The consequences of considering a phenomenon to be a medical problem may take radically different forms depending on whether the problem in question is correctly or incorrectly perceived as a medical issue. Neither indiscriminate acceptance of medicalization of subsequent areas of human existence, nor criticizing new medicalization cases just because they are medicalization (...) can be justified. The article: (i) identifies various consequences of both well-founded medicalization and over-medicalization; (ii) demonstrates that the issue of defining appropriate limits of medicine cannot be solved by creating an optimum model of health; (iii) proposes four guiding questions to help distinguish medicalization from over-medicalization. The article should foster a normative analysis of the phenomenon of medicalization and contribute to the bioethical reflection on the boundaries of medicine. (shrink)

This article investigates five kinds of vagueness in medicine: disciplinary, ontological, conceptual, epistemic, and vagueness with respect to descriptive-prescriptive connections. First, medicine is a discipline with unclear borders, as it builds on a wide range of other disciplines and subjects. Second, medicine deals with many indistinct phenomena resulting in borderline cases. Third, medicine uses a variety of vague concepts, making it unclear which situations, conditions, and processes that fall under them. Fourth, medicine is based on and produces uncertain knowledge and (...) evidence. Fifth, vagueness emerges in medicine as a result of a wide range of fact-value-interactions. The various kinds of vagueness in medicine can explain many of the basic challenges of modern medicine, such as overdiagnosis, underdiagnosis, and medicalization. Even more, it illustrates how complex and challenging the field of medicine is, but also how important contributions from the philosophy can be for the practice of medicine. By clarifying and, where possible, reducing or limiting vagueness, philosophy can help improving care. Reducing the various types of vagueness can improve clinical decision-making, informing individuals, and health policy making. (shrink)

Medicalization is frequently defined as a process by which some non-medical aspects of human life become to be considered as medical problems. Overdiagnosis, on the other hand, is most often defined as diagnosing a biomedical condition that in the absence of testing would not cause symptoms or death in the person’s lifetime. Medicalization and overdiagnosis are related concepts as both expand the extension of the concept of disease. They are both often used normatively to critique unwarranted or contested expansion of (...) medicine and to address health services that are considered to be unnecessary, futile, or even harmful. However, there are important differences between the concepts, as not all cases of overdiagnosis are medicalizations and not all cases of medicalizations are overdiagnosis. The objective of this article is to clarify the differences between medicalization and overdiagnosis. It will demonstrate how the subject matter of medicalization traditionally has been non-medical (social or cultural everyday life) phenomena, while the subject matter of overdiagnosis has been biological or biomolecular conditions or processes acknowledged being potentially harmful. They also refer to different types of uncertainty: medicalization is concerned with indeterminacy, while overdiagnosis is concerned with lack of prognostic knowledge. Medicalization is dealing with sickness (sick role) while overdiagnosis with disease. Despite these differences, medicalization and overdiagnosis are becoming more alike. Medicalization is expanding, encompassing the more “technical” aspects of overdiagnosis, while overdiagnosis is becoming more ideologized. Moreover, with new trends in modern medicine, such as P4 (preventive, predictive, personal, and participatory) medicine, medicalization will become all-encompassing, while overdiagnosis more or less may dissolve. In the end they may converge in some total “iatrogenization.” In doing so, the concepts may lose their precision and critical sting. (shrink)

In this article we explore how diagnostic and therapeutic technologies shape the lived experiences of illness for patients. By analysing a wide range of examples, we identify six ways that technology can (trans)form the experience of illness (and health). First, technology may create awareness of disease by revealing asymptomatic signs or markers (imaging techniques, blood tests). Second, the technology can reveal risk factors for developing diseases (e.g., high blood pressure or genetic tests that reveal risks of falling ill in the (...) future). Third, the technology can affect and change an already present illness experience (e.g., the way blood sugar measurement affects the perceived symptoms of diabetes). Fourth, therapeutic technologies may redefine our experiences of a certain condition as diseased rather than unfortunate (e.g. assisted reproductive technologies or symptom based diagnoses in psychiatry). Fifth, technology influences illness experiences through altering social-cultural norms and values regarding various diagnoses. Sixth, technology influences and changes our experiences of being healthy in contrast and relation to being diseased and ill. This typology of how technology forms illness and related conditions calls for reflection regarding the phenomenology of technology and health. How are medical technologies and their outcomes perceived and understood by patients? The phenomenological way of approaching illness as a lived, bodily being-in-the-world is an important approach for better understanding and evaluating the effects that medical technologies may have on our health, not only in defining, diagnosing, or treating diseases, but also in making us feel more vulnerable and less healthy in different regards. (shrink)

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’saccountability for reasonablenessconception, 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)

Prevention of age-related disorders is increasingly in focus of health policies, and it is hoped that early intervention on processes of deterioration can promote healthier and longer lives. New opportunities to slow down the aging process are emerging with new fields such as personalized nutrition. Data-intensive research has the potential to improve the precision of existing risk factors, e.g., to replace coarse-grained markers such as blood cholesterol with more detailed multivariate biomarkers. In this paper, we follow an attempt to develop (...) a new aging biomarker. The vision among the project consortium, comprising both research and industrial partners, is that the new biomarker will be predictive of a range of age-related conditions, which may be preventable through personalized nutrition. We combine philosophical analysis and ethnographic fieldwork to explore the possibilities and challenges of managing aging through bodily signs that are not straightforwardly linked to symptomatic disease. We document how the improvement of measurement brings about new conceptual challenges of demarcating healthy and unhealthy states. Moreover, we highlight that the reframing of aging as risk has social and ethical implications, as it is generative of normative notions of what constitutes successful aging and good citizenship. (shrink)

Recovery is a commonly used concept in both professional and everyday contexts. Yet despite its extensive use, it has not drawn much philosophical attention. In this paper, I question the common understanding of recovery, show how the concept is inadequate, and introduce new and much needed terminology. I argue that recovery glosses over important distinctions and even misrepresents the process of moving away from malady as "going back" to a former state of health. It does not invite important nuances needed (...) to distinguish between biomedical, phenomenological, and social perspectives. In addition, I claim that there are many conditions where we are making use of the concept of recovery, although the person recovered from the condition in question, has not regained the same degree of soundness. I show how the concept of recovery leads to conceptual discrepancies that can result in worsening patients' conditions. To gain a fuller understanding, I propose to rethink the direction of the process in question. I define the process of moving away from malady as a move forward towards a new state of soundness. I also suggest three terms, corresponding to different perspectives, to describe this movement forward: 'curing', 'healing', and 'habilitating'. This new terminology provides a more nuanced understanding of the states of both malady and soundness and an attentiveness as to how they differ and relate. (shrink)

Plusieurs aspects du modèle biopsychosocial promeuvent une approche humaniste en médecine. Cependant, Engel a explicitement rejeté un humanisme médical qui s’opposerait à la science. En adoptant une approche fondée sur la science des systèmes pour étudier les êtres humains, la santé et la maladie, Engel défend une approche scientifique pour améliorer la qualité des soins cliniques, ou autrement dit, une approche qui se prête à un examen scientifique de cette question.

We have a desire to discover and create order, and our constitution, including our rational faculties, indicates that we are predisposed for such productivity. This affinity for order and the establishment of order is fundamental to humans and naturally also leaves its mark on the medical discipline. When this profession is made subject to criticism, frequently in terms of well-used reproofs such as reductionism, reification and de-humanisation, this systematising productivity is invariably involved in some way or other. It is, however, (...) problematic that we rarely delve deeper and ask what order means, or reflect on its underlying, omnipresent and self-evident role. In order to approach this challenge, we initially and briefly place order in a conceptual and historical context. In what follows, we examine order explicitly, i.e. made an object of study, by taking a closer look at extensive multidisciplinary efforts to uncover the secrets of all its facets. Here we also try to identify some systems of order in medical science, including methodological and procedural order, which are indispensable as well as a source of problems. In the sections that follow, order is not defined as an explicit object of study, but comes to light in some exploratory and philosophising projects based on physics, mathematics and phenomenology. Each of these lets order and that which is ordered emerge in ways that may also shed light on opportunities and paradoxes in the medical domain. Key themes here include the Gordian knot of psyche – soma, the order of disorder and the patient as Other. (shrink)

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which (...) explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care. (shrink)

Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, especially in terms (...) of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. (shrink)

Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, especially in terms (...) of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. (shrink)

Personalized medicine aims to tailor disease prevention, diagnosis, and treatment to individuals on the basis of their genes, lifestyle and environments. Patient and interest organizations may potentially play an important role in the realization of PM. This paper investigates the views and perspectives on PM of a variety of PIOs. Semi-structured telephone interviews were conducted among leading representatives of 13 PIOs located in Europe and North-America. The data collected were analysed using a conventional content analysis approach. The PIO representatives supported (...) the realization of PM but feared that many financial, structural and organizational challenges may delay its realization. They encouraged strategies to modernize drug licencing mechanisms, develop research and data sharing infrastructures, and educate patients and health care professionals in PM. Notably, they emphasized the importance of developing PM in an equitable way and taking into consideration the patients’ needs, values and personal situation. Despite varying levels of awareness regarding PM, the PIO representatives expressed willingness to engage in the PM agenda and recommended that PIOs work closely with policy-makers to design PM in a way that truly addresses the needs and concerns of patients. PIOs have the potential to become central drivers of the PM agenda. Collaborations should be further developed between PIOs, researchers, drug developers and health care authorities. (shrink)

Simulations are used in very different contexts and for very different purposes. An emerging development is the possibility of using simulations to obtain a more or less representative reproduction of organs or even entire persons. Such simulations are framed and discussed using the term ‘digital twin’. This paper unpacks and scrutinises the current use of such digital twins in medicine and the ideas embedded in this practice. First, the paper maps the different types of digital twins. A special focus is (...) put on the concrete challenges inherent in the interactions between persons and their digital twin. Second, the paper addresses the questions of how far a digital twin can represent a person and what the consequences of this may be. Against the background of these two analytical steps, the paper defines first conditions for digital twins to take on an ethically justifiable form of representation. (shrink)

Slightly modified excerpt from the section 13.4 Zusammenfassung und Ausblick (translated into englisch): This chapter is based on an analysis of ethical debates on epigenetics and genome editing, debates, in which ethical arguments relating to future generations and justice play a central role. The analysis aims to contextualize new developments in genetic engineering, such as genome and epigenome editing, ethically. At the beginning, the assumptions of "genetic determinism," on which "genetic essentialism" is based, of "epigenetic determinism" as well as "genetic" (...) and "epigenetic exceptionalism" are analyzed and critically discussed. The remarks on the ethical discourse on epigenetics show that the notion of "epigenetic determinism" can sometimes be discerned not only in popular scientific discourse but also in ethical discourse. Ethical debates on epigenetics, however, have distanced themselves from this deterministic understanding. As a result, the focus in the ethical discourse on epigenetics shifts from responsibility for one's own health and that of subsequent generations to justice. What is meant here is justice, for example, in terms of access to healthy environmental conditions, regardless of whether these contribute to health with or without epigenetic agency. An analysis of the discourse on genome editing reveals that it is primarily germline interventions that are being ethically examined, with a concentration on the aspect of inheritance. The question is whether this is accompanied by an implicit "genetic determinism" or even a "genetic essentialism": the determinism could lie in the centrality of the aspect of heritability, since only genetic information is inherited. Does the aspect of heritability and genome modification play a more decisive role in debates on genome editing than the safety issue? Is the problem of embryos and their potential offspring not being able to consent to germline intervention given such a high priority because it is a genetic intervention? Under such a premise of "strong genetic determinism" supplemented by "epigenetic determinism," not only genome editing but also epigenome editing would be ethically relevant precisely because it too would have an influence on the genome. How this influence of genome editing and epigenome editing is ethically evaluated in each case therefore depends initially on whether the assumptions of "genetic" and "epigenetic determinism" are advocated. These assumptions are increasingly viewed critically in ethical discourse because they cannot be confirmed from a scientific perspective. In popular scientific debates in particular, however, they seem to persist, which ultimately also influences the public discussion. Since a broad public discussion is required especially on genome editing, a reflective approach to the various "-isms" analyzed in this chapter is central. DOI: 10.5771/9783748927242. (shrink)

Proponents of the emerging field of P4 medicine argue that computational integration and analysis of patient-specific “big data” will revolutionize our health care systems, in particular primary care-based disease prevention. While many ambitions remain visionary, steps to personalize medicine are already taken via personalized genomics, mobile health technologies and pilot projects. An important aim of P4 medicine is to enable disease prevention among healthy persons through detection of risk factors. In this paper, we examine the current status of P4 medicine (...) in light of historical and current challenges to predictive and preventive medicine, including overdiagnosis and overtreatment. Moreover, we ask whether it is likely that in silico integration of patient-specific data will be able to better deal such challenges and to turn risk predictions into disease-preventive actions in a wider social context. Given the lack of evidence that P4 medicine can tip the balance between benefits and harms in preventive medicine, we raise concerns about the current promotion of P4 medicine as a solution to the current challenges in public health. (shrink)

Proponents of the emerging field of P4 medicine argue that computational integration and analysis of patient-specific “big data” will revolutionize our health care systems, in particular primary care-based disease prevention. While many ambitions remain visionary, steps to personalize medicine are already taken via personalized genomics, mobile health technologies and pilot projects. An important aim of P4 medicine is to enable disease prevention among healthy persons through detection of risk factors. In this paper, we examine the current status of P4 medicine (...) in light of historical and current challenges to predictive and preventive medicine, including overdiagnosis and overtreatment. Moreover, we ask whether it is likely that in silico integration of patient-specific data will be able to better deal such challenges and to turn risk predictions into disease-preventive actions in a wider social context. Given the lack of evidence that P4 medicine can tip the balance between benefits and harms in preventive medicine, we raise concerns about the current promotion of P4 medicine as a solution to the current challenges in public health. (shrink)