This paper addresses the recent rise of the use of alternative medicine in Western countries. It offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory (...) theory defended here is then predicated on the idea that an extreme patient-centric model of medical practice that treats largely ignorant patients as consumers of medical products and services endowed with an essentially unrestricted power of freedom to choose treatments predictably leads serious and avoidable harms. Some important moral and epistemological consequences of this model are then articulated and corrective measures are suggested. (shrink)

There is a middle ground of imperfect knowledge in fields like medicine and the social sciences. It stands between our day-to-day relatively certain knowledge obtained from ordinary basic observation of regularities in our world and our knowledge from well-validated theories in the physical sciences. -/- The latter enable reliable prediction a great deal of the time of the happening of events never before experienced. The former enable prediction only of what has happened before and beyond that of educated guesses (...) which may sometimes prove right and others not when we test them. -/- The imperfection of our knowledge between those limits is a consequence of complexity. -/- Reductionist empiricism fails when faced with complexity but we all still have to live in a complex world where reductionist science cannot help us devise reliable theories from which we can predict the behaviour of the world around us. We cannot predict reliably. We can only monitor actively and manage actively the world around us if we want to try to control our environment. We have a limited prediction horizon. -/- Science and empiricism work well if we want to send Voyager I and II over 3 billion miles away but not for most other things. -/- In medicine how and when to apply probabilistic, conjectural, incomplete medical theories and explanations requires professional expertise, intuition and judgement (non scientific knowledge) which is essential. Medical diagnosis is a skill of predicting from knowledge of what has happened before that the past will recur in the current patient applying expertise and intuition from knowledge and experience of prior cases and probabilistic medical research and theory. The physician is left to an educated guess as to how the future will develop for the particular individual patient the focus of his or her current attention. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

ABSTRACT: Abstract Most modern knowledge is not science. The physical sciences have successfully validated theories to infer they can be used universally to predict in previously unexperienced circumstances. According to the conventional conception of science such inferences are falsified by a single irregular outcome. And verification is by the scientific method which requires strict regularity of outcome and establishes cause and effect. -/- Medicine, medical research and many “soft” sciences are concerned with individual people in complex heterogeneous populations. These (...) populations cannot be tested to demonstrate strict regularity of outcome in every individual. Neither randomised controlled trials nor observational studies in medicine are science in the conventional conception. Establishing and using medical and other “soft science” theories cannot be scientific. It requires conceptually different means: requiring expert judgement applying all available evidence in the relevant available factual matrix. -/- The practice of medicine is observational. Prediction of outcomes for the individual requires professional expertise applying available medical knowledge and evidence. Expertise in any profession can only be acquired through experience. Prior cases are the fundament of knowledge and expertise in medicine. Case histories, studies and series can provide knowledge of extremely high reliability applicable to establishing reliable general theories and falsifying others. Their collation, study and analysis should be a priority in medicine. Their devaluation as evidence, the failure to apply their lessons, the devaluation of expert professional judgement and the attempt to emulate the scientific method are all historic errors in the theory and practice of modern medicine. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...) A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Part of a symposium devoted to ‘Prediction, Understanding, and Medicine’, in which Alex Broadbent argues that the nature of medicine is determined by its competences, i.e., which things it can do well. He argues that, although medicine cannot cure well, it can do a good job of enabling people not only to understand states of the human organism and of what has caused them, but also to predict future states of it. From this Broadbent concludes that (...) class='Hi'>medicine is (at least in part) essentially a practice of understanding and predicting, not curing. In reply to this bold position, I mount two major criticisms. First, I maintain that the reasons Broadbent gives for doubting that medicine can cure provide comparable reason for doubting that medicine can provide an understanding; roughly, the best explanation of why medicine cannot reliably cure is that we still lack much understanding of health and disease. Second, I object to the claim that a practice is medical only if it facilitates understanding and prediction. Although Broadbent has brought to light certain desirable purposes of medicine that are under-appreciated, my conclusion is that he has not yet provided enough reason to think that understanding and prediction are essential to it. Instead of supposing that medicine has an essence, in fact, I suggest that its nature is best understood in terms of a property cluster. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s (...) outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

The first part of a symposium devoted to Alex Broadbent's essay titled ‘Prediction, Understanding and Medicine’, this article notes the under-development of a variety of issues in the philosophy of medicine that transcend bioethics and the long-standing debates about the nature of health/illness and of evidence-based medicine. It also indicates the importance of drawing on non-Western, and particularly African, traditions in addressing these largely metaphysical and epistemological matters.

Even though the evidence‐based medicine movement (EBM) labels mechanisms a low quality form of evidence, consideration of the mechanisms on which medicine relies, and the distinct roles that mechanisms might play in clinical practice, offers a number of insights into EBM itself. In this paper, I examine the connections between EBM and mechanisms from several angles. I diagnose what went wrong in two examples where mechanistic reasoning failed to generate accurate predictions for how a dysfunctional mechanism would respond (...) to intervention. I then use these examples to explain why we should expect this kind of mechanistic reasoning to fail in systematic ways, by situating these failures in terms of evolved complexity of the causal system(s) in question. I argue that there is still a different role in which mechanisms continue to figure as evidence in EBM: namely, in guiding the application of population‐level recommendations to individual patients. Thus, even though the evidence‐based movement rejects one role in which mechanistic reasoning serves as evidence, there are other evidentiary roles for mechanistic reasoning. This renders plausible the claims of some critics of evidencebased medicine who point to the ineliminable role of clinical experience. Clearly specifying the ways in which mechanisms and mechanistic reasoning can be involved in clinical practice frames the discussion about EBM and clinical experience in more fruitful terms. (shrink)

Mismatch is a prominent concept in evolutionary medicine and a number of philosophers have published analyses of this concept. The word ‘mismatch’ has been used in a diversity of ways across a range of sciences, leading these authors to regard it as a vague concept in need of philosophical clarification. Here, in contrast, we concentrate on the use of mismatch in modelling and experimentation in evolutionary medicine. This reveals a rigorous theory of mismatch within which the term ‘mismatch’ (...) is indeed used in several ways, not because it is ill-defined but because different forms of mismatch are.distinguished within the theory. Contemporary evolutionary medicine has unified the idea of ‘evolutionary mismatch’, derived from the older idea of ‘adaptive lag’ in evolution, with ideas about mismatch in development and physiology derived from the Developmental Origins of Health and Disease (DOHaD) paradigm. A number of publications in evolutionary medicine have tried to make this theoretical framework explicit. We build on these to present the theory in as simple and general a form as possible. We introduce terminology, largely drawn from the existing literature, to distinguish the different forms of mismatch. This integrative theory of mismatch captures how organisms track environments across space and time on multiple scales in order to maintain an adaptive match to the environment, and how failures of adaptive tracking lead to disease. Mismatch is a productive organising concept within this theory which helps researchers articulate how physiology, development and evolution interact with one another and with environmental change to explain health outcomes. (shrink)

Introduction: Anatomy education known as being among the most significant components of medical education exists in the first and second semester lecture contents of medicine faculties. The considering of medicine faculty students viewpoints about anatomy education is important for improving anatomy education and teaching methods. In addition, the evaluation of interest and awareness of students to anatomy in postgraduate specialization preferences provides to predict researcher number which will be study in this area. -/- Materials and Methods: In this (...) study, it was proposed to evaluate the opinions of medicine faculty students about anatomy education and the view points and the awareness to anatomy in postgraduate specialization education. This study was performed on Gaziantep University second and third year medicine faculty students (n= 340). The data were obtained by feedback method with a questionnaire which consist of 30 question. -/- Results: It was found that the students chiefly benefited from lecture notes while they have been studied to theoretical and practical part of the anatomy individually. No significant correlation was found among general average grade and lengths of anatomy study of students (r= 0.097, p= 0.074). Fifty percent (%50) of students thought that don’t use of cadaver is always a deficiency in anatomy education. It was seen that the postgraduate career preferences of the students of 39.4% was internal medicine departments. The rate of students desiring the postgraduate anatomy specialization education was 1.2%. -/- Conclusion: In conclusion, the achieved results may will be beneficial for improving the new educational strategies related anatomy, resolving the present deficiencies and increasing the tendency to postgraduate anatomy education. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A neural (...) network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Diagnoses are central to the practice of medicine, where they serve a variety of functions for clinicians, patients, and society. They aid communication, explain symptoms, inform predictions, guide therapeutic interventions, legitimize sickness, and authorize access to resources. Insofar as psychiatry is a discipline whose practice is shaped by medical conventions, its diagnoses are sometimes presented as if they serve the same sorts of function as diagnoses in bodily medicine. However, there are philosophical problems that cast doubt on whether (...) the functions of psychiatric diagnoses can legitimately be considered to be equivalent to those of medical diagnoses. The aim of this chapter is to explicate some of these problems, particularly conceptual and epistemological problems pertaining to the roles of diagnoses as explanations. I begin with an overview of the various functions that medical diagnoses normally serve and suggest that many of these functions receive justificatory support from the explanatory roles of the diagnoses. I then present issues regarding the epistemic functions of psychiatric diagnoses and how these issues have featured in the arguments of prominent critics of psychiatry. (shrink)

Abstract: Lung cancer is the top reason of cancer-associated deaths globally. Surgery is the typical treatment for early-stage non-small cell lung cancer (NSCLC). Advancement in the knowledge of the biology of non-small cell lung cancer has shown molecular evidence used for systemic cancer therapy aiming metastatic disease, with a significant impact on patients’ overall survival (OS) and eminence of life. Though, a biopsy of overt metastases is an invasive technique restricted to assured positions and not effortlessly satisfactory in the clinic. (...) The examination of peripheral blood samples of cancer patients embodies a new basis of cancer-derived material, recognized as liquid biopsy, and its constituents (circulating tumour cells (CTCS), circulating free DNA (cfDNA), exosomes, and tumour-educated platelets (TEP)) may be gotten from nearly any body liquids. These constituents have shown to imitate features of the status of both the primary and metastatic diseases, aiding the clinicians to go towards a tailored medicine. In this paper, the reasons of lung cancer will be recognized and the risk elements that initiated the increase of infection, for instance Smoking, Disclosure to secondhand smoke, Disclosure to radon gas, Disclosure to asbestos and other compounds, Family past history of lung cancer, and decrease of the spread of disease and approaches of handling and prevention of lung cancer. (shrink)

Seminal work intended to found a new field of integrative Noetic Science is summarized. Until now the philosophy of Biological Mechanism has ruled medicine and psychology. Penrose claims, AA scientific world-view which does not profoundly come to terms with the problem of conscious mind can have no serious pretensions of completeness@. A noetic action principle synonymous with the historic concept of élan vital is introduced as the basis of a Continuous State Conscious Universe (CSCU). The least unit of CSCU (...) superspace defines Awareness as a fundamental physical quantity like charge in electrodynamics. This cosmological context reveals the origin of complexity in self-organized living-systems wherein the physical basis of qualia is formalized. The dynamics of this teleological action principle, mediated by a unitary noetic field pervading all biochemical species, optimizes the state of well-being through homeostasis and provides the fundamental basis for developing a Moral Psychology. (shrink)

The conditions animals experience during the early developmental stages of their lives can have critical ongoing effects on their future health, welfare, and proper development. In this paper we draw on evolutionary theory to improve our understanding of the processes of developmental programming, particularly Predictive Adaptive Responses (PAR) that serve to match offspring phenotype with predicted future environmental conditions. When these predictions fail, a mismatch occurs between offspring phenotype and the environment, which can have long-lasting health and welfare effects. (...) Examples include metabolic diseases resulting from maternal nutrition and behavioral changes from maternal stress. An understanding of these processes and their evolutionary origins will help in identifying and providing appropriate developmental conditions to optimize offspring welfare. This serves as an example of the benefits of using evolutionary thinking within veterinary science and we suggest that in the same way that evolutionary medicine has helped our understanding of human health, the implementation of evolutionary veterinary science (EvoVetSci) could be a useful way forward for research in animal health and welfare. (shrink)

Patient preference predictors aim to solve the moral problem of making treatment decisions on behalf of incapacitated patients. This commentary on a case of an unrepresented patient at the end of life considers 3 related problems of such predictors: the problem of restricting the scope of inputs to the models (the “scope” problem), the problem of weighing inputs against one another (the “weight” problem), and the problem of multiple reasonable solutions to the scope and weight problems (the “multiple reasonable models” (...) problem). Each of these problems poses challenges to reliably implementing patient preference predictors in important, high-stakes health care decision making. This commentary also suggests a way forward. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an average error (...) of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological (...) and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics. (shrink)

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, (...) since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. (shrink)

A minimal essentialism (‘intrinsic biological essentialism’) about natural kinds is required to explain the projectability of human science terms. Human classifications that yield robust and ampliative projectable inferences refer to biological kinds. I articulate this argument with reference to an intrinsic essentialist account of HPC kinds. This account implies that human sciences (e.g., medicine, psychiatry) that aim to formulate predictive kind categories should classify biological kinds. Issues concerning psychiatric classification and pluralism are examined.

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of (...) wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Suppose that a patient is receiving treatment options from her doctor. In one case, the doctor says, “the surgery has a 90% survival rate.” Now, suppose the doctor instead said, “the procedure has a 10% mortality rate.” Predictably, the patient is more likely to consent on the first description and more likely to dissent on the second. This is an example of a framing effect. A framing effect occurs when “the description of [logically-equivalent] options in terms of gains (positive frame) (...) rather than losses (negative frame) elicits systematically different choices.” Framing effects are ubiquitous, but they are particularly troublesome in medicine. Many worry that there is tension between valuing informed consent and using framing effects in clinical settings. In this paper, I answer this question: if an individual is subject to a framing effect when she gives her consent, does this undermine the validity of her consent? I argue that framing effects do not undermine consent in general. (shrink)

The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it (...) helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources. (shrink)

The article analyzes the technological shifts which took place in the second half of the twentieth and early twenty-first centuries and predict the main shifts in the next half a century. On the basis of the analysis of the latest achievements in medicine, bio- and nanotechnologies, robotics, ICT and other technological directions and also on the basis of the opportunities provided by the theory of production revolutions the authors present a detailed analysis of the latest production revolution which is (...) denoted as ‘Cybernetic’. There are given some forecasts about its development in the nearest five decades and up to the end of twenty-first century. It is shown that the development of various self-regulating systems will be the main trend of this revolution. The article gives a detailed analysis of the future breakthroughs in medicine, and also in bio- and nanotechnologies in terms of the development of self-regulating systems with their growing ability to select optimal modes of functioning as well as of other characteristics of the Cybernetic Revolution (resources and energy saving, miniaturization, and individualization). (shrink)

Braun explores the use of digital twin technology in medicine with a particular emphasis on the question of how such simulations can represent a person.1 In defining some first conditions for ethically justifiable forms of representation of digital twins, he argues that digital twins do not threaten an embodied person, as long as that person retains control over their simulated representation via dynamic consent, and ideally with the option to choose both form and usage of the simulation. His thoughtful (...) elaboration provides insight into the challenges inherent in interactions between a person and their digital twin, emphasising the modes of control required to respect personal modes of freedom. This individual-centric approach to the question of representation leaves out the important ethical consideration of ensuring that all, not merely some, can be represented. Braun describes dynamic consent as a necessary mode of control, arguing that where a person is unable to give consent, ‘such simulations threaten to become illegitimate representations. They would then shift the kind of interaction from representation to illegitimate forms of prediction or surveillance and thereby could lead to infringements to individual modes of freedom.’ Consequently, the implementation of digital twin technology in healthcare would seem to exclude the most vulnerable members of society from participation in and benefiting from medical innovation, …. (shrink)

Digital phenotyping will potentially enable earlier detection and prediction of mental illness by monitoring human interaction with and through digital devices. Notwithstanding its promises, it is certain that a person’s digital phenotype will at times be at odds with their first-person testimony of their psychological states. In this paper, we argue that there are features of digital phenotyping in the context of psychiatry which have the potential to exacerbate the tendency to dismiss patients’ testimony and treatment preferences, which can be (...) instances of epistemic injustice. We first explain what epistemic injustice is, and why it is argued to be an extensive problem in health and disability settings. We then explain why epistemic injustice is more likely to apply with even greater force in psychiatric contexts, and especially where digital phenotyping may be involved. Finally, we offer some tentative suggestions of how epistemic injustice can be minimised in digital psychiatry. (shrink)

Background Healthcare is predicated on the use of biotechnology and medical technology, both of which are indispensable in diagnosis, treatment, and most aspects of patient care. It is therefore imperative that justifications for use of new technologies are appropriate, with the technologies working as advertised. In this paper, I consider philosophical accounts of how such justifications are made. Methods Critical philosophical reflection and analysis. Results I propose that justification in many prominent accounts is based on the designer’s professional experience and (...) on expert testimony. I argue, however, that professional designers are not in a position to justify a new biotechnology or medical device if the justification is based on testimony or past experience of presumably similar technologies. I argue (1) that similarity judgments offered by instantaneous experts cannot be viewed as contributing (epistemically) to evidential justification of new and unproven technologies; and (2) that designers and manufacturers cannot endorse a technology’s effective function in a patient-care context until it has been successfully used in that context. Conclusion I show that an expert’s past professional experiences can never predict or justify the impact of a novel technology on human health. This is because any new technology leads to the introduction of new mechanisms with unprecedented functions. The new technology therefore needs to be studied in situ and justified as a newly created mechanism within the relevant healthcare setting. Ultimately, justifications of this type rely on the scientific community and society engaging in repeated experimentation and observation of the technology, and confirming its successful use. (shrink)

The monograph presents the ideas about the main changes that occurred in the development of technologies from the emergence of Homo sapiens till present time and outlines the prospects of their development in the next 30–60 years and in some respect until the end of the twenty-first century. What determines the transition of a society from one level of development to another? One of the most fundamental causes is the global technological transformations. Among all major technological breakthroughs in history the (...) most important are three production revolutions: 1) the Agrarian Revolution; 2) the Industrial Revolution; and 3) the Cybernetic one. The book introduces the theory of production revolutions which is a new valuable explanatory paradigm that analyzes causes and trends of dramatic shifts in historical process. The authors describe the course of technological transformations in history and demonstrate a possible application of the theory to explain the present and forthcoming technological changes. They analyze the technological shifts which took place in the second half of the twentieth and early twenty-first centuries and forecast the main shifts in the next half a century. On this basis the authors present a detailed analysis of the latest production revolution which is denoted as ‘Сybernetic’. They make some predictions about its development in the nearest five decades and up to the end of the twenty-first century and show that the development of various self-regulating systems will be the main trend of this revolution. The authors argue that the transition to the starting final phase of the Cybernetic Revolution (in the 2030–2040s) will first occur in the field of medicine (in some its innovative branches). In future we will deal with the started convergence of innovative technologies which will form the system of MANBRIC-technologies (i.e. the technological paradigm based on medicine, additive, nano- and bio- technologies, robotics, IT and cognitive technologies). The monograph gives an outline of the future breakthroughs in medicine and some other technologies (between the 2010s and 2070s). (shrink)

This paper analyzes the underlying tendencies and attitudes toward reproductive medicine borrowing the Nietzschean concepts of nihilism: “death of God” with secularization; “will to power” with reproductive liberty and technological power; and the race of “supermen” with transhumanism. Medical science has advanced in leaps and bounds. In some way, technical innovations have given us unprecedented power to manipulate the way we reproduce. The indiscriminant use of medical technology is backed by a warped notion of human freedom. With secularization in (...) the West, freedom has taken on greater significance in society, but with a heavy emphasis on individual choices and rights. As technology joins forces with sexual liberty, it is not difficult to understand why the public accepts the latest novelty from the reproductive industry. As a result, many find Catholic teaching behind the times and incomprehensible, if not downright anti-scientific. In fact, this coupling of reproductive liberty (will) with reproductive technology (power) echoes the famous dictum “will to power” Nietzsche predicted would characterize post-modern societies. When liberty becomes absolute and technology unchecked, transhumanism is the logical outcome. As a response to these nihilistic tendencies, the paper will end with a critique drawing from theological insights. (shrink)

From the Philosophies journal program, one of the main aims of the journal is to help establish a new unity in diversity in human knowledge, which would include both “Wissen” (i.e., “Wissenschaft”) and “sc¯ıre” (i.e., “science”). As is known, “Wissenshaft” (the pursuit of knowledge, learning, and scholarship) is a broader concept of knowledge than “science”, as it involves all kinds of knowledge,including philosophy, and not exclusively knowledge in the form of directly testable explanations and predictions. The broader notion of scholarship (...) incorporates an understanding and articulation of the role of the learner and the process of the growth of knowledge and its development, rather than only the final product and its verification and validation. In other words, it is a form of knowledge that is inclusive of both short-term and long-term perspectives; it is local and global, critical and hypothetical (speculative), breaking new ground. This new synthesis or rather re-connection of knowledge is expected to resonate with basic human value systems, including cultural values. Since knowledge tends to spontaneously fragment while it grows, we take existing diversity as a resource and a starting point for a new synthesis. The idea of broad, inclusive knowledge is in fact not so new. From the beginning, natural philosophy included all contemporary knowledge about nature. Newton was a natural philosopher, as were Bohr, Einstein, Prigogine, Weizsäcker, and Wheeler—to name but a few. Today, the unifying picture of the natural/physical world is sorely missing among the isolated silos of particular scientific domains, each with its own specific ontologies, methodologies, and epistemologies. From the profound need for connected and common knowledge, new trends towards synthesis have emerged in the last decades. One major theme is complexity, especially when applied to biology or medicine, which helps us to grasp the importance of connectedness between present-day disparate pieces of knowledge—frameworks, theories, approaches, etc. Related to this is the emergence of network science, which studies structures of nodes (actors) and edges as connections between them. This book is connecting work on contemporary natural philosophy and its connections with existing philosophies, sciences and other knowledge fields. (shrink)

Drug regulation is fraught with inductive risk. Regulators must make a prediction about whether or not an experimental pharmaceutical will be effective and relatively safe when used by typical patients, and such predictions are based on a complex, indeterminate, and incomplete evidential basis. Such inductive risk has important practical consequences. If regulators reject an experimental drug when it in fact has a favourable benefit/harm profile, then a valuable intervention is denied to the public and a company’s material interests are needlessly (...) thwarted. Conversely, if regulators approve an experimental drug when it in fact has an unfavourable benefit/harm profile, then resources are wasted, people are needlessly harmed, and other potentially more effective treatments are underutilized. Given that such regulatory decisions have these practical consequences, non-epistemic values about the relative importance of these consequences impact the way such regulatory decisions are made (similar to the analysis of laboratory studies on the toxic effects of dioxins presented in Douglas [2000]). To balance the competing demands of the pertinent non-epistemic values, regulators must perform what I call an “inductive risk calculus.” At least in the American context this inductive risk calculus is not well-managed. (shrink)

Lilly was one of the greatest scientists and pioneers on the limits of human possibility but after his death a collective amnesia has descended and he is now almost forgotten. His Wiki is good but inevitably incomplete so here are a few missing details and viewpoints. Lilly was a generation (or more) ahead of his time. He is almost single-handedly responsible for the great interest in dolphins (which led to the Marine Mammal Protection Act in the USA and helped to (...) found the animal rights movement). In 1958 he noted that the brains of elephants and cetaceans were larger than ours, that we should not abuse them and that it was one our most important projects to communicate with them. He invented sensory isolation tanks (at NIMH in 1954) and used them extensively with and without powerful psychoactive drugs at a time when it was thought that either the brain would shut down or one would go insane if external stimuli were eliminated. He created methods for implanting electrodes in mammal brains and was planning to do it to himself. He was one of the first to make serious use of computers in bioscience research and created the hardware and software to make the first attempts to communicate with dolphins. He self experimented with dangerous physiological investigations in high altitude medicine for the military during WW2, took LSD with dolphins and movie stars, submitted himself to the rigors of various forms of yoga and of Arica training, and taught classes at Esalen. He was a computer pioneer who forsaw the rapid advances in A.I. and it's inevitable clash with humans. He was the first one to investigate the bizarre psychedelic ketamine (" vitamin K "), and his results (published in the two last chapters of his book `The Scientist`) are still the best data on the dose/effect relation of any psychedelic on one person. It cured his lifelong daily migraine headaches (see http://www.conspiracyarchive.com/UFOs/Gorightly.htm). And all this happened before most of us were born! He had courage, honesty and integrity that is rare anywhere and almost nonexistent in science. His goal was to find the ultimate truth about everything and he went about as far as anyone ever has. He had little patience with the stupid and hypocritical games one has to play to fit into monkey society. Of course the reaction of the establishment was predictable. He left the NIMH and was never given any government or academic support for the last 35 years of his life. His paper and comments at a conference on sensory deprivation were removed from the published version. He was not invited to government sponsored symposia on dolphins (he had refused to help develop them as weapons), though he clearly knew more about them than anyone in the world. He liked to live and work on the edge and few could keep up with him, as his books make clear. If you have read some of his other books it will be much easier going. He was a pioneer in consciousness research and pushed the boundaries of our understanding of who we are and what we might become. Among other things he catalogs the various states reached by drugs, meditation, and isolation, tries to determine their significance, and suggests how to use them. I very briefly review and comment on his life and work. -/- Those interested in all my writings in their most recent versions may download from this site my e-book ‘Philosophy, Human Nature and the Collapse of Civilization Michael Starks (2016)- Articles and Reviews 2006-2016’ by Michael Starks First Ed. 662p (2016). -/- All of my papers and books have now been published in revised versions both in ebooks and in printed books. -/- Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B071HVC7YP. -/- The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle--Articles and Reviews 2006-2016 (2017) https://www.amazon.com/dp/B071P1RP1B. -/- Suicidal Utopian Delusions in the 21st century: Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B0711R5LGX -/- . (shrink)

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in (...) this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined. (shrink)

To succeed, political science usually requires either prediction or contextual historical work. Both of these methods favor explanations that are narrow-scope, applying to only one or a few cases. Because of the difficulty of prediction, the main focus of political science should often be contextual historical work. These epistemological conclusions follow from the ubiquity of causal fragility, under-determination, and noise. They tell against several practices that are widespread in the discipline: wide-scope retrospective testing, such as much large-n statistical work; lack (...) of emphasis on prediction; and resources devoted to ‘pure theory’ divorced from frequent empirical application. I illustrate, via Donatella della Porta’s work on political violence, the important role that is still left for theory. I conclude by assessing the scope for political science to offer policy advice. (shrink)

This chapter wades into the growing discussion surrounding embodied cognition and predictive processing. After surveying a recent debate between Jakob Hohwy and Andy Clark, it articulates two outstanding issues facing discussions of compatibility. It argues that headway on these issues can be made by drawing on the resources of philosophy of science.

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable (...) approximations to Solomonoff prediction do not always converge. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive (...) feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...) predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of (...) sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

This paper examines a looming reproducibility crisis in the core of the hard sciences. Namely, it concentrates on molecular modeling and simulation (MMS), a family of methods that predict properties of substances through computing interactions on a molecular level and that is widely popular in physics, chemistry, materials science, and engineering. The paper argues that in order to make quantitative predictions, sophisticated models are needed which have to be evaluated with complex simulation procedures that amalgamate theoretical, technological, and social factors (...) – leading to problems with reproducibility. Thus, for methodological reasons, the predictive success causes a reproducibility problem. (shrink)