Biomedical ontologies exist to serve integration of clinical and experimental data, and it is critical to their success that they be put to widespread use in the annotation of data. How, then, can ontologies achieve the sort of user-friendliness, reliability, cost-effectiveness, and breadth of coverage that is necessary to ensure extensive usage? Methods: Our focus here is on two different sets of answers to these questions that have been proposed, on the one hand in medicine, by the SNOMED CT (...) community, and on the other hand in biology, by the OBO Foundry. We address more specifically the issue as to how adherence to certain development principles can advance the usability and effectiveness of an ontology or terminology resource, for example by allowing more accurate maintenance, more reliable application, and more efficient interoperation with other ontologies and information resources. Results: SNOMED CT and the OBO Foundry differ considerably in their general approach.Nevertheless, a general trend towards more formal rigor and cross-domain interoperability can be seen in both and we argue that this trend should be accepted by all similar initiatives in the future. Conclusions: Future efforts in ontology development have to address the need for harmonization and integration of ontologies across disciplinary borders, and for this, coherent formalization of ontologies is a prerequisite. (shrink)

We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the 19th century, and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the 20th century. We show how aspects of this idea, particularly those relating to the use of (...) the term 'concept' in ontology development, influenced SNOMED CT and other medical terminologies. Against this background we then show how the Foundational Model of Anatomy, the Gene Ontology, Basic Formal Ontology and other OBO Foundry ontologies came into existence and discuss their role in the development of contemporary biomedical informatics. (shrink)

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop (...) and use ontologies and terminologies in biomedicine. The centerpiece of the National Center for Biomedical Ontology is a web-based resource known as BioPortal. BioPortal makes available for research in computationally useful forms more than 270 of the world's biomedical ontologies and terminologies, and supports a wide range of web services that enable investigators to use the ontologies to annotate and retrieve data, to generate value sets and special-purpose lexicons, and to perform advanced analytics on a wide range of biomedical data. (shrink)

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding (...) semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource providing details on the people, policies, and issues being addressed in association with OBI. (shrink)

Biomedical technologies can increasingly be used not only to combat disease, but also to augment the capacities or traits of normal, healthy people – a practice commonly referred to as biomedical enhancement. Perhaps the best‐established examples of biomedical enhancement are cosmetic surgery and doping in sports. But most recent scientific attention and ethical debate focuses on extending lifespan, lifting mood, and augmenting cognitive capacities.

In biomedical ethics, there is widespread acceptance of moral realism, the view that moral claims express a proposition and that at least some of these propositions are true. Biomedical ethics is also in the business of attributing moral obligations, such as “S should do X.” The problem, as we argue, is that against the background of moral realism, most of these attributions are erroneous or inaccurate. The typical obligation attribution issued by a biomedical ethicist fails to truly (...) capture the person's actual obligations. We offer a novel argument for rife error in obligation attribution. The argument starts with the idea of an epistemic burden. Epistemic burdens are all of those epistemic obstacles one must surmount in order to achieve some aim. Epistemic burdens shape decision-making such that given two otherwise equal options, a person will choose the option that has the lesser of epistemic burdens. Epistemic burdens determine one's potential obligations and, conversely, their non-obligations. The problem for biomedical ethics is that ethicists have little to no access to others' epistemic burdens. Given this lack of access and the fact that epistemic burdens determine potential obligations, biomedical ethicists often can only attribute accurate obligations out of luck. This suggests that the practice of attributing obligations in biomedical ethics is rife with error. To resolve this widespread error, we argue that this practice should be abolished from the discourse of biomedical ethics. (shrink)

In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting a particular O/T as (...) a standard for data exchange purposes is often seen by the end-user as a function of the number of applications using that vocabulary (and, by extension, the size of the user base). Furthermore, the adoption of an O/T as an interoperability standard requires confidence in its stability and guaranteed continuity as a resource. (shrink)

This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.

Biomedical research plays a crucial role in advancing public health through the discovery of treatments and the development of health technologies and policies. However, the process of translating biomedical and health research into practical applications is complex and often spans several months or even years. Studies show that the duration of this translation process can significantly impact the progress of biomedical science, from basic scientific discoveries to medical approvals and eventual implementation. The speed of translation varies depending (...) on factors such as scientific complexity, available funding, government regulations, and the involvement of diverse fields of expertise. Real-world cases highlight both successful and slow transitions in research translation, shedding light on the obstacles that hinder efficient progress. These cases emphasize the challenges that must be addressed to improve the translation process. Furthermore, the investigation illustrates how biomedical discoveries contribute to the creation of new health policies, which ultimately lead to better public health outcomes. To overcome existing challenges, the research suggests three key strategic initiatives that could accelerate the translation of biomedical research. Effectively managing this critical period is essential for ensuring quicker patient access to lifesaving medical innovations. (shrink)

Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This (...) paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data. (shrink)

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic (...) similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. (shrink)

1. Biomedical sciences are fast-growing fields with unprecedented speed of research outputs, especially in the quantities of papers. Philosophers aiming to study ongoing biomedical changes face cha...

We argue that there are neither scientific nor social reasons to require gathering ethno-racial data, as defined in the US legal regulations if researchers have no prior hypotheses as to how to connect this type of categorisation of human participants of clinical trials with any mechanisms that could explain alleged interracial health differences and guide treatment choice. Although we agree with the normative perspective embedded in the calls for the fair selection of participants for biomedical research, we demonstrate that (...) current attempts to provide and elucidate the criteria for the fair selection of participants, in particular, taking into account ethno-racial categories, overlook important epistemic and normative challenges to implement the results of such race-sorting requirements. We discuss existing arguments for and against gathering ethno-racial statistics for biomedical research and present a new one that refers to the assumption that prediction is epistemically superior to accommodation. We also underline the importance of closer interaction between research ethics and the methodology of biomedicine in the case of population stratifications for medical research, which requires weighing non-epistemic values with methodological constraints. (shrink)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, (...) which is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (shrink)

In this article, we analyse how researchers use the categories of race and ethnicity with reference to genetics and genomics. We show that there is still considerable conceptual “messiness” (despite the wide-ranging and popular debate on the subject) when it comes to the use of ethnoracial categories in genetics and genomics that among other things makes it difficult to properly compare and interpret research using ethnoracial categories, as well as draw conclusions from them. Finally, we briefly reconstruct some of the (...) biases of reductionism to which geneticists (as well as other researchers referring to genetic methods and explanations) are particularly exposed to, and we analyse the problem in the context of the biologization of ethnoracial categories. Our work constitutes a novel, in-depth contribution to the debate about reporting race and ethnicity in biomedical and health research. First, we reconstruct the theoretical background assumptions about racial ontology which researchers implicitly presume in their studies with the aid of a sample of recent papers published in medical journals about COVID-19. Secondly, we use the typology of the biases of reductionism to the problem of biologization of ethnoracial categories with reference to genetics and genomics. (shrink)

The goal of the OBO (Open Biomedical Ontologies) Foundry initiative is to create and maintain an evolving collection of non-overlapping interoperable ontologies that will offer unambiguous representations of the types of entities in biological and biomedical reality. These ontologies are designed to serve non-redundant annotation of data and scientific text. To achieve these ends, the Foundry imposes strict requirements upon the ontologies eligible for inclusion. While these requirements are not met by most existing biomedical terminologies, the latter (...) may nonetheless support the Foundry’s goal of consistent and non-redundant annotation if appropriate mappings of data annotated with their aid can be achieved. To construct such mappings in reliable fashion, however, it is necessary to analyze terminological resources from an ontologically realistic perspective in such a way as to identify the exact import of the ‘concepts’ and associated terms which they contain. We propose a framework for such analysis that is designed to maximize the degree to which legacy terminologies and the data coded with their aid can be successfully used for information-driven clinical and translational research. (shrink)

. In the evolving landscape of healthcare, the efficient processing of biomedical signals is critical for real-time diagnosis and personalized treatment. Conventional cloud-based AI systems for biomedical signal processing face challenges such as high latency, bandwidth consumption, and data privacy concerns. Edge computing, which brings data processing closer to the source, has emerged as a potential solution to these limitations. However, optimizing AI models for edge devices, which often have limited computational resources, remains a challenge. This paper proposes (...) an innovative approach to optimize AI models for biomedical signal processing by leveraging reinforcement learning (RL) techniques within edge computing environments. Reinforcement learning offers the potential to dynamically adapt model parameters based on real-time feedback from the edge devices, optimizing the trade-offs between model accuracy, resource utilization, and processing speed. The proposed system is designed to operate with high efficiency on edge devices, enabling faster signal processing while ensuring energy efficiency and maintaining diagnostic accuracy. A detailed review of existing literature highlights the benefits and challenges of edge computing in biomedical applications, the role of reinforcement learning in dynamic optimization, and the limitations of traditional AI models in constrained environments. The proposed system methodology integrates RL for model optimization, focusing on edge devices' unique constraints in terms of power, memory, and processing capacity. Simulation results demonstrate the improved efficiency and accuracy of biomedical signal processing with optimized AI models in edge computing setups, showing significant improvements in real-time diagnostic applications. (shrink)

An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) (...) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease. (shrink)

It is only by fixing on agreed meanings of terms in biomedical terminologies that we will be in a position to achieve that accumulation and integration of knowledge that is indispensable to progress at the frontiers of biomedicine. Standardly, the goal of fixing meanings is seen as being realized through the alignment of terms on what are called ‘concepts’. Part I addresses three versions of the concept-based approach – by Cimino, by Wüster, and by Campbell and associates – and (...) surveys some of the problems to which they give rise, all of which have to do with a failure to anchor the terms in terminologies to corresponding referents in reality. Part II outlines a new, realist solution to this anchorage problem, which sees terminology construction as being motivated by the goal of alignment not on concepts but on the universals (kinds, types) in reality and thereby also on the corresponding instances (individuals, tokens). We outline the realist approach, and show how on its basis we can provide a benchmark of correctness for terminologies which will at the same time allow a new type of integration of terminologies and electronic health records. We conclude by outlining ways in which the framework thus defined might be exploited for purposes of diagnostic decision-support. (shrink)

To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

Motivated by a recent trend that advocates a reassessment of the aim of medical science and clinical practice, this paper investigates the epistemic aims of biomedical research. Drawing on contemporary discussions in epistemology and the philosophy of science, along with a recent study on scurvy, this paper (1) explores the concept of understanding as the aim of scientific inquiry and (2) establishes a framework that will guide the examination of its forms in biomedical research. Using the case of (...) Tuberculosis (TB), (3) it is argued that grasping a mechanistic explanation is crucial for reaching a threshold of understanding at which we may speak of an objectual, biomedical understanding of TB. (shrink)

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Biomedical Informatics: We Are What We Publish", written by Peter L. Elkin, Steven H. Brown, and Graham Wright. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Elkin et al. paper. In subsequent issues the discussion can continue through letters to the editor.

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used (...) to carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used (...) to carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

Publication ethics is an important aspect of both the research and publication enterprises. It is particularly important in the field of biomedical science because published data may directly affect human health. In this article, we examine publication ethics policies in biomedical journals published in Central and Eastern Europe. We were interested in possible differences between East European countries that are members of the European Union (Eastern EU) and South-East European countries (South-East Europe) that are not members of the (...) European Union.The most common ethical issues addressed by all journals in the region were redundant publication, peer review process, and copyright or licensing details. Image manipulation, editors’ conflicts of interest and registration of clinical trialswere the least common ethical policies. Three aspects were significantly morecommon in journals published outside the EU: statements on the endorsement of international editorial standards, contributorship policy, and image manipulation.On the other hand, copyright or licensing information were more prevalent in journals published in the Eastern EU. The existence of significant differences amongbiomedical journals’ ethical policies calls for further research and active measuresto harmonize policies across journals. On the other hand, copyright or licensing information were more prevalent in journals published in the Eastern EU. The existence of significant differences among biomedical journals’ ethical policies calls for further research and active measures to harmonize policies across journals. (shrink)

: Advancements in biomedical signal processing have unlocked new possibilities for personalized healthcare. However, the sheer volume of data generated in modern medical environments, coupled with the complexity of interpreting these signals in real-time, poses significant challenges. This paper explores the integration of artificial intelligence (AI), particularly deep learning, with big data analytics to create a powerful predictive analytics framework for biomedical signal processing. By leveraging AI, we aim to automate the extraction of significant features from high-dimensional data (...) and improve diagnostic accuracy in detecting anomalies, such as cardiac arrhythmias, neurological disorders, and respiratory conditions. (shrink)

It is widely believed that a conservative moral outlook is opposed to biomedical forms of human enhancement. In this paper, I argue that this widespread belief is incorrect. Using Cohen’s evaluative conservatism as my starting point, I argue that there are strong conservative reasons to prioritise the development of biomedical enhancements. In particular, I suggest that biomedical enhancement may be essential if we are to maintain our current evaluative equilibrium (i.e. the set of values that undergird and (...) permeate our current political, economic, and personal lives) against the threats to that equilibrium posed by external, non-biomedical forms of enhancement. I defend this view against modest conservatives who insist that biomedical enhancements pose a greater risk to our current evaluative equilibrium, and against those who see no principled distinction between the forms of human enhancement. (shrink)

The pathbreaking scientific advances of recent years call for a new philosophical consideration of the fundamental categories of biology and its neighboring disciplines. Above all, the new information technologies used in biomedical research, and the necessity to master the continuously growing flood of data that is associated therewith, demand a profound and systematic reflection on the systematization and classification of biological data. This, however, demands robust theories of basic concepts such as kind, species, part, whole, function, process, fragment, sequence, (...) expression, boundary, locus, environment, system, and so on. Concepts which belong to the implicit stock of knowledge of every biologist. They amount to a dimension of biological reality which remains constant in the course of biological evolution and whose theoretical treatment requires contemporary analogues of the tools developed in traditional Aristotelian metaphysics. To provide the necessary theories and definitions is a task for philosophy, which is thus called upon to play an important role as intermediary between biology and informatics. (shrink)

While there is a steadily growing literature on epistemic injustice in healthcare, there are few discussions of the role that biomedical technologies play in harming patients in their capacity as knowers. Through an analysis of newborn and pediatric genetic and genomic sequencing technologies (GSTs), I argue that biomedical technologies can lead to epistemic injustice through two primary pathways: epistemic capture and value partitioning. I close by discussing the larger ethical and political context of critical analyses of GSTs and (...) their broader implications for just and equitable healthcare delivery. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical data, enabling (...) faster and more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

In this paper, we discuss the processes of racialisation on the example of biomedical research. We argue that applying the concept of racialisation in biomedical research can be much more precise, informative and suitable than currently used categories, such as race and ethnicity. For this purpose, we construct a model of the different processes affecting and co-shaping the racialisation of an individual, and consider these in relation to biomedical research, particularly to studies on hypertension. We finish with (...) a discussion on the potential application of our proposition to institutional guidelines on the use of racial categories in biomedical research. (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)

Business models for biomedical research prescribe decentralization due to market selection pressures. I argue that decentralized biomedical research does not match four normative philosophical models of the role of values in science. Non-epistemic values affect the internal stages of for-profit biomedical science. Publication planning, effected by Contract Research Organizations, inhibits mechanisms for transformative criticism. The structure of contracted research precludes attribution of responsibility for foreseeable harm resulting from methodological choices. The effectiveness of business strategies leads to overrepresentation (...) of profit values versus the values of the general public. These disconnects in respect to the proper role of values in science results from structural issues ultimately linked to the distinct goals of business versus applied science, and so it seems likely that disconnects will also be found in other dimensions of attempts to combine business and science. The volume and integration in the publishing community of decentralized biomedical research imply that the entire community of biomedical research science cannot match the normative criteria of community-focused models of values in science. Several proposals for changing research funding structure might successfully relieve market pressures that drive decentralization. (shrink)

The European project European and Latin American Systems of Ethics Regulation of Biomedical Research Project (EULABOR) has carried out the first comparative analysis of ethics regulation systems for biomedical research in seven countries in Europe and Latin America, evaluating their roles in the protection of human subjects. We developed a conceptual and methodological framework defining ‘ethics regulation system for biomedical research’ as a set of actors, institutions, codes and laws involved in overseeing the ethics of biomedical (...) research on humans. This framework allowed us to develop comprehensive national reports by conducting semi-structured interviews to key informants. These reports were summarised and analysed in a comparative analysis. The study showed that the regulatory framework for clinical research in these countries differ in scope. It showed that despite the different political contexts, actors involved and motivations for creating the regulation, in most of the studied countries it was the government who took the lead in setting up the system. The study also showed that Europe and Latin America are similar regarding national bodies and research ethics committees, but the Brazilian system has strong and noteworthy specificities. (shrink)

What proper role should considerations of risk, particularly to research subjects, play when it comes to conducting research on human enhancement in the military context? We introduce the currently visible military enhancement techniques (1) and the standard discussion of risk for these (2), in particular what we refer to as the ‘Assumption’, which states that the demands for risk-avoidance are higher for enhancement than for therapy. We challenge the Assumption through the introduction of three categories of enhancements (3): therapeutic, preventive, (...) and pure enhancements. This demands a revision of the Assumption (4), alongside which we propose some further general principles bearing on how to balance risks and benefits in the context of military enhancement research. We identify a particular type of therapeutic enhancements as providing a more responsible path to human trials of the relevant interventions than pure enhancement applications. Finally, we discuss some possible objections to our line of thought (5). While acknowledging their potential insights, we ultimately find them to be unpersuasive, at least provided that our proposal is understood as fully non-coercive towards the candidates for such therapeutic enhancement trials. (shrink)

Biomedical research is increasingly a matter of the navigation through large computerized information resources deriving from functional genomics or from the biochemistry of disease pathways. To make such navigation possible, controlled vocabularies are needed in terms of which data from different sources can be unified. One of the most influential developments in this regard is the so-called Gene Ontology, which consists of controlled vocabularies of terms used by biologists to describe cellular constituents, biological processes and molecular functions, organized into (...) hierarchies via the relation of class subsumption. Here we seek to provide a rigorous account of the logic of classification that underlies GO and similar biomedical ontologies. Drawing on Aristotle, we develop a system of axioms and definitions for the treatment of biological classes and instances. (shrink)

The contribution of women to the development of societies and medicine around the world cannot be overstated. Their contribution to medicine is great; this is seen, in particular, in their role among other physicians, obstetricians, nurses, herbalists, and assistant physicians. Despite the significant contribution of women to medicine and health care, the have often been largely omitted in the history of medicine and other scientific literature. Therefore, my study contains an obvious novelty: the urgent need to consider the diverse role (...) of women in the history of Ghana's health care, hence my recent empirical study on the contribution of women to biomedical care in Obuasi, in the Asante Region of Ghana. (shrink)

An accurate classification of bacteria is essential for the proper identification of patient infections and subsequent treatment decisions. Multi-Locus Sequence Typing (MLST) is a genetic technique for bacterial classification. MLST classifications are used to cluster bacteria into clonal complexes. Importantly, clonal complexes can serve as a biological species concept for bacteria, facilitating an otherwise difficult taxonomic classification. In this paper, we argue for the inclusion of terms relating to clonal complexes in biomedical ontologies.

The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In (...) the naming of genes, proteins, and other molecular structures, considerable efforts are under way to reduce the effects of the different naming conventions which have been spawned by different groups of researchers. Electronic patient records, too, increasingly involve the use of standardized terminologies, and tremendous efforts are currently being devoted to the creation of terminology resources that can meet the needs of a future era of personalized medicine, in which genomic and clinical data can be aligned in such a way that the corresponding information systems become interoperable. (shrink)

Our philosophical understanding of mental illness is being shaped by neuroscience. However, it has the paradoxical effect of igniting two radically opposed groups of philosophical views. On one side, skepticism and denialism assume that, lacking clear biological mechanisms and etiologies for most mental illnesses, we should infer they are constructions best explained by means of social factors. This is strongly associated with medical nihilism: it considers psychiatry more harmful than benign. On the other side of the divide, naturalism and reductionism (...) are on the look for failures in the biological functioning of the organism whenever a genuine mental illness occurs. Psychiatry as currently practiced, accordingly, exhibits the gaps of an ongoing research programme; a yet to be completed neuroscience would link mental illnesses with identifiable biological mechanisms. Both sides of this divide claim to be fostered by scientific discoveries and advances in neuroscience, when taken at face value. Against this background, we argue instead for a modest view. To that end, we draw attention to some nuances in the scientific realism debate. While contending that neuroscientific theories and models aim to provide true representations of their target systems, and can justifiably claim to have attained some, we argue that our confidence should not be placed beforehand in specific features of these scientific representations. Hence, it would be unwarranted to extract morals for psychiatry from posits (or their absence) in neuroscientific explanations of mental illnesses. To illustrate our position, we examine some recent discoveries in neuroscience concerning bipolar disorder. We conclude by linking our topic to a broader issue in the philosophy of medicine: insofar as psychiatry is a biomedical specialty, its classifications of health and disease are guided by pragmatic concerns, as well as by scientific discoveries. (shrink)

In recent years, the combination of Artificial Intelligence (AI) and Blockchain technology has garnered significant attention, especially in the healthcare domain. With the increasing reliance on biomedical signal processing for disease diagnosis and treatment, ensuring the security, privacy, and integrity of data has become paramount. Biomedical signals, including electrocardiograms (ECG), electroencephalograms (EEG), and other physiological data, often contain sensitive information. AI models have shown great promise in processing and interpreting these signals, enabling accurate disease detection and personalized healthcare. (...) However, the potential for data tampering, unauthorized access, and privacy concerns pose significant challenges. Blockchain, with its decentralized, tamper-resistant, and secure framework, offers a solution to these challenges. By integrating Blockchain with AI for biomedical signal processing, data can be securely stored, verified, and accessed, ensuring that healthcare providers can rely on the integrity and accuracy of the signals used in AI-based diagnostics. This paper explores the role of Blockchain-enhanced AI solutions for secure biomedical signal processing and data integration, addressing both the technical and ethical challenges in this space. We propose a system that leverages Blockchain to manage the secure transfer and storage of biomedical signals while utilizing AI to analyze and interpret these signals in real time. Blockchain provides an immutable ledger for auditing and validating data provenance, ensuring that the data remains secure throughout the healthcare pipeline. The integration of these two technologies not only enhances the security of biomedical data but also enables more trustworthy AI-driven diagnostics and decision-making in healthcare. (shrink)

The growing commercialization of scientific research has raised important concerns about industry bias. According to some evidence, so-called industry bias can affect the integrity of the science as well as the direction of the research agenda. I argue that conceptualizing industry’s influence in scientific research in terms of bias is unhelpful. Insofar as industry sponsorship negatively affects the integrity of the research, it does so through biasing mechanisms that can affect any research independently of the source of funding. Talk about (...) industry bias thus offers no insight into the particular epistemic shortcomings at stake. If the concern is with the negative effects that industry funding can have on the research agenda, conceptualizing this influence as bias obscures the ways in which such impact is problematic and limits our ability to offer solutions that can successfully address the concerns raised by the growing role of private funding in science. (shrink)

The meeting focused on uses of ontologies, with a special focus on spatial ontologies, in addressing the ever increasing needs faced by biology and medicine to cope with ever expanding quantities of data. To provide effective solutions computers need to integrate data deriving from myriad heterogeneous sources by bringing the data together within a single framework. The meeting brought together leaders in the field of what are called "top-level ontologies" to address this issue, and to establish strategies among leaders in (...) the field of biomedical ontology for the creation of interoperable biomedical ontologies which will serve the goal of useful data integration. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle patterns (...) in large datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

The recent progression in AI, nanomedicine and robotics have increased concerns about ethics, policy and law. The increasing complexity and hybrid nature of AI and nanotechnologies impact the functionality of “law in action” which can lead to legal uncertainty and ultimately to a public distrust. There is an immediate need of collaboration between Central Asian biomedical scientists, AI engineers and academic lawyers for the harmonization of AI, nanomedicines and robotics in Central Asian legal system.

Ontologies describe reality in specific domains in ways that can bridge various disciplines and languages. They allow easier access and integration of information that is collected by different groups. Ontologies are currently used in the biomedical sciences, geography, and law. A Biomedical Ethics Ontology would benefit members of ethics committees who deal with protocols and consent forms spanning numerous fields of inquiry. There already exists the Ontology for Biomedical Investigations (OBI); the proposed BMEO would interoperate with OBI, (...) creating a powerful information tool. We define a domain ontology and begin to construct a BMEO, focused on the process of evaluating human research protocols. Finally, we show how our BMEO can have practical applications for ethics committees. This paper describes ongoing research and a strategy for its broader continuation and cooperation. (shrink)

As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s (...) motivation, content, structure, adoption, and governance approach. (shrink)

This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.