Precision medicine is motivated by the insight that patients and their problems show great variability and ideally should be treated in way that accounts for the individual’s biology and context. Realizing this vision rests on the development of new model systems that can recapitulate the physiological context beyond genomics and account for relevant characteristics of individual patients. Organoids are 3D cell cultures derived from stem cells or dissociated primary tissue (e.g., a tumor), sometimes combined via microfluidics into a so-called organ-on-chip model. Organoids and organ-on-chip models are hoped to present new opportunities for direct translation from bench to bedside, by bridging the gap between in vitro models and specific in vivo targets. These models are in the scientific literature described as “miniature organs”, “diseases in a dish”, “patients-on-chips”, and are envisioned to lead to a new “one-patient paradigm” in medicine. The aim of this paper is to provide an empirically informed philosophical analysis of what is expressed in such concepts and visions for the future. Through a qualitative content analysis and ethnographic field work, we unpack what the “vision of precision” entails in organoid and organ-on-chip research and analyze the ontological and epistemic implications of different versions of this vision. We then examine an application that has already been implemented in some clinical contexts, namely the use of tumor organoids for patient-specific drug screening. By allowing for “real-time” testing targeted treatments on organoids developed from a specific patient’s cancer cells, these personalized models challenge traditional understandings of preclinical models and clinical trials. In exploring the potentials and challenges of the new model systems, we uncover underlying assumptions about what characterizes disease and constitutes evidence when the scope of preclinical models narrows down to specific patients. We show how epistemic uncertainties about translational inferences from bench to bedside relate to ontological uncertainties about how fine-grained disease categories should be understood. Moreover, we show how epistemic and ethical implications intersect when cancer patients become urgently dependent on ongoing laboratory research.

Data science, and related data infrastructures and analytic tools, are frequently invoked as a major factor underpinning contemporary transformations in medical research, diagnosis and treatment. This paper discusses whether and how this is happening, and what the implications may be for philosophical understandings of the production, assessment and use of medical evidence. To this aim I consider the role of data science in tackling COVID-related illness and hospitalization, focusing on four areas that have proved critical to the medical response to the pandemic: 1. The development of data technologies and infrastructures to monitor COVID patients, for instance by checking levels of oxygen saturation in the blood, and related efforts to determine the extent to which frequent patient assessments help prevent hospitalization and death; 2. The collection, linkage and analysis of patient data by doctors and other health professionals (both in and outside the hospitals) to ensure effective and prompt insights into the emerging symptoms and long-term effects of infections caused by different COVID variants; 3. The use of data extracted from social services and other non-medical sources to support predictive models of COVID transmission, thus informing public health and treatment guidelines; and 4. The significance of data availability for the development and testing of COVID vaccines, and particularly the ways in which existing data sharing mechanisms (such as genomic databases) were redeployed and greatly expanded to inform small scale, non-clinical studies in several locations around the world, while at the same time underpinning the set-up of large-scale clinical trials. From consideration of these areas, I argue that the data science had a transformative effect on medical research on COVID-19, leading to an acceleration of knowledge production and significant changes in the evaluation of what counts as reliable evidence. Such transformation originated not solely from the deployment of novel methods and instruments for computational data mining and modelling, but also – and perhaps most fundamentally – from the diversity and scope of the data sources considered as potential evidence for medical knowledge and interventions, and the related challenges to existing standards for how evidence is produced, circulated and validated. The evidential power accrued by data produced by medical doctors and frontline hospital staff became incontrovertible, providing ammunition to already existing critiques of the hierarchy of evidence entrenched within the evidence-based medicine (EBM) movement. The need to recognise and value data coming from patients and doctors, compounded by the imperative to act swiftly to tackle the pandemic emergency, provided a strong incentive to review the structure and temporalities of randomised controlled trials, the relation between RCT results and other data, and the ways in which data circulation and exchange is regulated and fostered. This resulting shifts in evidential standards are ongoing. What remains unchallenged – and if anything has been strengthened by reliance on data analytics - is the dependence of publicly funded medical research and services on pharmaceutical companies and other private enterprises focused on the health sector.

According to a view that is gaining traction in current philosophy of science, what best describes the aim of scientific inquiries is not truth or knowledge about some target phenomenon, but understanding, which is taken to be a distinct cognitive accomplishment. At the same time, scientific inquiry is thought to be characterized by a certain systematicity that sets it apart from everyday inquiries. This permits a gradual progression from prescientific (or nonscientific) to scientific inquiries and grants scientific inquires a higher degree of systematicity without rendering the commonsense counterpart unsystematic. In light of these two views, we may conceptualize scientific medicine as a systematic inquiry that aims at a particular type of (medical) understanding. Due to the significant diversity that characterizes scientific endeavors, we may expect that what qualifies as constituting proper understanding is to a certain degree context-sensitive and can take on different forms depending on the nature of the scientific field and the features of its subject matter. If so, then we have at least some initial reasons for thinking that understanding within the context of medicine might differ in various ways from understanding in physics or chemistry. A better comprehension of the nature of understanding in medicine merits sustained philosophical attention, and this talk is dedicated to clarifying this matter. The talk falls into three parts. The first part describes in more detail what it means to understand something, distinguishes types of understanding, and links a type of understanding (i.e., objectual understanding) to explanations. The second part proceeds to investigate what objectual understanding of a disease (i.e., biomedical understanding) requires by considering the case of scurvy from the history of medicine. The main hypothesis here is that grasping a correct mechanistic explanation of a condition is a necessary condition for biomedical understanding of that condition. The third part of the talk argues that biomedical understanding is necessary, but not sufficient for understanding in a clinical context (i.e., clinical understanding). The hypothesis is that clinical understanding combines biomedical understanding of a disease or pathological condition with a personal understanding of the patient with an illness. It will be shown that in many cases, clinical understanding necessitates adopting a particular second-personal stance and using cognitive resources in addition to those involved in biomedical understanding. The attempt to support this hypothesis will include revisiting the distinction between “understanding” and “explanation” familiar from debates concerning methodological principles in the humanities and social science.

Few would deny that contemporary western medicine is scientific, but what exactly is implied by this claim? Recent work in philosophy of science has brought research on the demarcation problem to bear on this question and has argued that medicine is a science. Authors disagree on what demarcates scientific medicine as a science from pseudosciences like homeopathy, whether it is scientific medicine’s systematicity, its reliance on clinical trials, or something else. However, this framing misses out on the historical dimension of scientific medicine, which was emerging as the dominant medical tradition in the West around the turn of the twentieth century. Not only do several proposed demarcation criteria fail to capture this timing (systematic diagnostic classification came earlier, the boom in clinical trials came later), but they also fail to recognize that scientific medicine in the nineteenth century involved new and more intimate relationships between medicine and independent sciences. Rather than asking what makes contemporary western medicine a science (assuming it is indeed one), in this talk I ask what makes contemporary western medicine ‘scientific medicine’, what demarcates scientific medicine from non-scientific medicine. Probing the latter question reveals that scientific medicine is a shifting model of medicine in history and today. In brief, scientific medicine results from the integration of medical practice with particular medical sciences and it models itself after these sciences in various respects. Different medical sciences have vied for this role. In the late 1800s, laboratory sciences, especially physiology, biochemistry, and bacteriology, characterized scientific medicine, which was taken by experimental physiologist Claude Bernard to mean ‘experimental medicine’. In the late 1900s, epidemiology played a large role in reshaping scientific medicine, which was rebranded as ‘evidence-based medicine’, with ‘evidence’ standing in for epidemiological evidence. Today, molecular genetics and computer/data science promise to remake scientific medicine in their image under the labels of ‘precision medicine’ and ‘deep medicine’, respectively. Whether they succeed will depend on whether precision medicine or deep medicine involve merely using new knowledge and technologies towards pre-existing medical ends, or rather imply a more radical reimagining of core medical concepts and reasoning (e.g. personalized diagnosis and treatment that do away with diagnostic categories and population data, medical AI that replaces much of the cognitive work of the clinician). These historical and potential future shifts in scientific medicine are the source of important new philosophical and practical problems. For instance, the remaking of scientific medicine in the image of epidemiology by the late 1900s brought with it the problems of multifactorial etiology and medical risk through the application of epidemiological methods and concepts to medicine (namely, multivariate statistics and risk-based outcome measures, respectively). These developments are not captured by asking whether medicine is a science. They only come into focus when we recognize that scientific medicine derives its scientific character from other sciences and that over time different sciences – from experimental physiology to epidemiology – have competed to claim the mantle of ‘the science of medicine’.