While medicine is solidly grounded on scientific areas such as biology and chemistry, some argue that it is in its essence not a science at all. With medicine playing a substantial societal role, addressing questions about the scientific nature of medicine is of obvious urgency. This paper takes on such a task and starts by consulting the literature on the “demarcation” problem in the philosophy of science. Learning from failures of earlier approaches, it proposes that we adopt a Deflated Approach, (...) which acknowledges that “science” is a family resemblance concept that admits differences of degrees to nonscientific undertakings. Then, drawing on Paul Hoyningen-Huene’s (Systematicity: The nature of science, Oxford University Press, Oxford, 2013); (Synthese 196: 907–928, 2019) account of systematicity and Alexander Bird’s (Synthese 196: 863–879, 2019) analysis of examples from the history of medicine, the paper argues that medicine meets the requirement for systematicity on all dimensions and thereby qualifies as a science. The paper then considers and defuses two objections. First, it is shown that nonepistemic differences linked to the distinctive duality of medicine do not warrant thinking that medicine is not science. Second, against some recent criticism (Oreskes in Synthese 196: 881–905, 2019), the paper uses homeopathy as an example to show that (synchronic and diachronic) systematicity can succeed as a demarcation criterion. (shrink)

In the process of scientific discovery, knowledge ampliation is pursued by means of non-deductive inferences. When ampliative reasoning is performed, probabilities cannot be assigned objectively. One of the reasons is that we face the problem of the unconceived alternatives: we are unable to explore the space of all the possible alternatives to a given hypothesis, because we do not know how this space is shaped. So, if we want to adequately account for the process of knowledge ampliation, we need to (...) develop an account of the process of scientific discovery which is not exclusively based on probability calculus. We argue that the analytic view of the method of science advocated by Cellucci is interestingly suited to this goal, since it rests on the concept of plausibility. In this perspective, in order to account for how probabilities are in fact assigned in uncertain contexts and knowledge ampliation is really pursued, we have to take into account plausibility-based considerations. (shrink)

Scientific and medical practice both relate to and differ from each other, as do discussions of how to handle decisions under uncertainty in the laboratory and clinic respectively. While studies of science have pointed out that scientific practice is more complex and messier than dominant conceptions suggest, medical practice has looked to the rigour of scientific and statistical methods to address clinical uncertainty. In this article, we turn to epistemological studies of the laboratory to highlight how clinical practice already has (...) strategies for dealing with messiness. We draw on Hans-Jo ̈rg Rheinberger’s Toward a History of Epistemic Things, in which he invokes the metaphor of a spider’s web to explain the role of tacit practices in experimental biochemistry for helping practitioners manage messiness. We argue that diagnostic practices in clinical medicine employ similar, albeit codified, procedures to evaluate epistemic significance, ensure sensitivity to the unforeseen, and allow focused grounds for action. We consider three practices: (a) the pre-set structure of medical records, ensuring broad coverage in initial anamnesis, (b) the use of lists of differential diagnoses and ongoing ‘anchoring and adjusting’ as inquiry progresses, and (c) shared decision-making as an occasion to synthesize empirical evidence and reopen inquiry for potential missed information. We end by suggesting that while philosophy of medicine may learn from laboratory epistemology, the sciences may learn something from medical practice. (shrink)

In his latest book, Hoyningen-Huene develops a theory of the ‘nature of science’ which distinguishes science from ‘other forms of knowledge’ on the basis that science has a higher degree of systematicity than them. He analyses systematicity along nine dimensions. I argue that Hoyningen-Huene fails to offer a cogent account of the nature of science and that the chief weakness of his view is that he leaves out of the picture the whole issue of the epistemic warrant for scientific theories (...) and their claim to a better understanding of the nature of reality. (shrink)

In this article, I reply to the preceding articles by Naomi Oreskes, Chrysostomos Mantzavinos, Brad Wray, Sarah Green, Alexander Bird, and Timothy Lyons. These articles contain a number of objections and suggestions concerning systematicity theory, as developed in my book ystematicity: The Nature of Science.

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead (...) that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. (shrink)