Results for 'Medical data'

980 found
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  1. (1 other version)Enabling posthumous medical data donation: an appeal for the ethical utilisation of personal health data.Jenny Krutzinna, Mariarosaria Taddeo & Luciano Floridi - 2019 - Science and Engineering Ethics 25 (5):1357-1387.
    This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, similar to the way in which they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute at the University of Oxford. Ten arguments are provided to support the need to (...)
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  2.  17
    (1 other version)Ethical Medical Data Donation: A Pressing Issue.Luciano Floridi & Jenny Krutzinna - 2019 - In Peter Dabrock, Matthias Braun & Patrik Hummel (eds.), The Ethics of Medical Data Donation. Springer Verlag.
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  3. The Use of Machine Learning Methods for Image Classification in Medical Data.Destiny Agboro - forthcoming - International Journal of Ethics.
    Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging (...)
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  4.  16
    Rough set theory-based feature _selection and FGA-NN classifier for medical data classification (14th edition).Rajendran Sugumar - 2019 - Int. J. Business Intelligence and Data Mining 14 (3):322-358. Translated by Rajendran Sugumar.
    The prediction of heart disease is a difficult task, which needs much experience and knowledge. In order to reduce the risk of heart disease prediction, in this paper we proposed a rough set theory-based feature selection and FGA-NN classifier. The overall process of the proposed system consists of two main steps, such as: 1) feature reduction; 2) heart disease prediction. At first, the kernel fuzzy c-means clustering with roughest theory (KFCMRS) algorithm is applied to the high dimensional data to (...)
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  5. Medical Privacy and Big Data: A Further Reason in Favour of Public Universal Healthcare Coverage.Carissa Véliz - 2019 - In Philosophical Foundations of Medical Law. pp. 306-318.
    Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, (...)
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  6. When data drive health: an archaeology of medical records technology.Colin Koopman, Paul D. G. Showler, Patrick Jones, Mary McLevey & Valerie Simon - 2022 - Biosocieties 17 (4):782-804.
    Medicine is often thought of as a science of the body, but it is also a science of data. In some contexts, it can even be asserted that data drive health. This article focuses on a key piece of data technology central to contemporary practices of medicine: the medical record. By situating the medical record in the perspective of its history, we inquire into how the kinds of data that are kept at sites of (...)
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  7.  95
    Journal of Medical Ethics at 50: a data-driven history.Vilius Dranseika, Piotr Bystranowski & Tomasz Żuradzki - forthcoming - Journal of Medical Ethics.
    In this paper, we take a data-driven approach to analyse intellectual trends over the first five decades of theJournal of Medical Ethics(JME). Our data set, comprising all texts published in theJMEsince 1975, reveals not only the most distinctive topics of theJMEin comparison to other key journals with similar profiles but also diachronic fluctuations in the prominence of certain topics. Overall, the distribution of topics shifted gradually, with each editorial period at theJMEshowing continuity with its immediate predecessor. However, (...)
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  8. Not the doctor’s business: Privacy, personal responsibility and data rights in medical settings.Carissa Véliz - 2020 - Bioethics 34 (7):712-718.
    This paper argues that assessing personal responsibility in healthcare settings for the allocation of medical resources would be too privacy-invasive to be morally justifiable. In addition to being an inappropriate and moralizing intrusion into the private lives of patients, it would put patients’ sensitive data at risk, making data subjects vulnerable to a variety of privacy-related harms. Even though we allow privacy-invasive investigations to take place in legal trials, the justice and healthcare systems are not analogous. The (...)
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  9. “Giving something back”: a systematic review and ethical enquiry into public views on the use of patient data for research in the United Kingdom and the Republic of Ireland.Jessica Stockdale, Jackie Cassell & Elizabeth Ford - 2019 - Wellcome Open Research 3 (6).
    Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and (...)
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  10. Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?Daniel Susser & Laura Y. Cabrera - 2023 - American Journal of Bioethics Neuroscience 15 (2):122-133.
    The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)
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  11. Medical Image Classification with Machine Learning Classifier.Destiny Agboro - forthcoming - Journal of Computer Science.
    In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated (...)
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  12. Enabling the Nonhypothesis-Driven Approach: On Data Minimalization, Bias, and the Integration of Data Science in Medical Research and Practice.C. W. Safarlou, M. van Smeden, R. Vermeulen & K. R. Jongsma - 2023 - American Journal of Bioethics 23 (9):72-76.
    Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...
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  13. Do medical schools teach medical humanities? Review of curricula in the United States, Canada and the United Kingdom.Jeremy Howick, Lunan Zhao, Brenna McKaig, Alessandro Rosa, Raffaella Campaner, Jason Oke & Dien Ho - 2021 - Journal of Evaluation in Clinical Practice (1):86-92.
    Rationale and objectives: Medical humanities are becoming increasingly recognized as positively impacting medical education and medical practice. However, the extent of medical humanities teaching in medical schools is largely unknown. We reviewed medical school curricula in Canada, the UK and the US. We also explored the relationship between medical school ranking and the inclusion of medical humanities in the curricula. -/- Methods: We searched the curriculum websites of all accredited medical schools (...)
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  14. Indignity of Nazi data: reflections on the utilization of illicit research.Iman Farahani & Joel Janhonen - 2024 - Medicine, Health Care and Philosophy (3):381-387.
    Human rights may feel self-apparent to us, but less than 80 years ago, one of the most advanced countries at the time acted based on an utterly contrary ideology. The view of social Darwinism that abandoned the idea of the intrinsic value of human lives instead argued that oppression of the inferior is not only inevitable but desirable. One of the many catastrophic outcomes is the medical data obtained from inhuman experiments at concentration camps. Ethical uncertainty over whether (...)
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  15. The commercialization of patient data in Canada: ethics, privacy and policy.Sheryl Spithoff, Jessica Stockdale, Robyn Rowe, Brenda McPhail & Nav Persaud - 2022 - Canadian Medical Association Journal 194 (3).
    KEY POINTS In Canada, commercial data brokers collect deidentified patient data from pharmacies, private drug insurers, the federal government and medical clinics without patient consent. Although pharmaceutical companies are the data brokers’ primary customers, academics and nonprofit and public entities also use commercial data sets, given the absence of a coordinated public approach to collecting these data across Canada. Risks of commercialized patient data include loss of anonymity, surveillance and marketing, discrimination and violation (...)
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  16.  67
    Drug Recommendation System in Medical Emergencies using Machine Learning.S. Venkatesh - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-21.
    In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. (...)
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  17. Key ethical challenges in the European Medical Information Framework.Luciano Floridi, Christoph Luetge, Ugo Pagallo, Burkhard Schafer, Peggy Valcke, Effy Vayena, Janet Addison, Nigel Hughes, Nathan Lea, Caroline Sage, Bart Vannieuwenhuyse & Dipak Kalra - 2019 - Minds and Machines 29 (3):355-371.
    The European Medical Information Framework project, funded through the IMI programme, has designed and implemented a federated platform to connect health data from a variety of sources across Europe, to facilitate large scale clinical and life sciences research. It enables approved users to analyse securely multiple, diverse, data via a single portal, thereby mediating research opportunities across a large quantity of research data. EMIF developed a code of practice to ensure the privacy protection of data (...)
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  18. The problem of the consent for the processing of health data, particularly for biomedical research purposes, from the perspective of fundamental rights protection in the Digital Era.Joaquín Sarrión Esteve - 2018 - Revista de Derecho y Genoma Humano: Genética, Biotecnología y Medicina Avanzada = Law and the Human Genome Review: Genetics, Biotechnology and Advanced Medicine 48:107-132.
    Health data processing fields face ethical and legal problems regarding fundamental rights. As we know, patients can benefit in the Digital Era from having health or medical information available, and medical decisions can be more effective with a better understanding of clinical histories, medical and health data thanks to the development of Artificial Intelligence, Internet of Things and other Digital technologies. However, at the same time, we need to guarantee fundamental rights, including privacy ones. The (...)
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  19. Data and Safety Monitoring Board and the Ratio Decidendi of the Trial.Roger Stanev - 2015 - Journal of Philosophy, Science and Law 15:1-26.
    Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my (...)
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  20. Direct Medical Costs of Tetanus, Dengue, and Sepsis Patients in an Intensive Care Unit in Vietnam.Trinh Manh Hung, Nguyen Van Hao, Lam Minh Yen, Angela McBride, Vu Quoc Dat, H. Rogier van Doorn, Huynh Thi Loan, Nguyen Thanh Phong, Martin J. Llewelyn, Behzad Nadjm, Sophie Yacoub, C. Louise Thwaites, Sayem Ahmed, Nguyen Van Vinh Chau, Hugo C. Turner & Vietnam I. C. U. Translational Applications Laboratory - 2022 - Frontiers in Public Health 10:893200.
    Background: Critically ill patients often require complex clinical care by highly trained staff within a specialized intensive care unit (ICU) with advanced equipment. There are currently limited data on the costs of critical care in low-and middle-income countries (LMICs). This study aims to investigate the direct-medical costs of key infectious disease (tetanus, sepsis, and dengue) patients admitted to ICU in a hospital in Ho Chi Minh City (HCMC), Vietnam, and explores how the costs and cost drivers can vary (...)
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  21. Precision Medicine and Big Data: The Application of an Ethics Framework for Big Data in Health and Research.G. Owen Schaefer, E. Shyong Tai & Shirley Sun - 2019 - Asian Bioethics Review 11 (3):275-288.
    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 (...)
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  22. Speech acts and medical records: The ontological nexus.Lowell Vizenor & Barry Smith - 2004 - In Jana Zvárová (ed.), Proceedings of the International Joint Meeting EuroMISE 2004.
    Despite the recent advances in information and communication technology that have increased our ability to store and circulate information, the task of ensuring that the right sorts of information gets to the right sorts of people remains. We argue that the many efforts underway to develop efficient means for sharing information across healthcare systems and organizations would benefit from a careful analysis of human action in healthcare organizations. This in turn requires that the management of information and knowledge within healthcare (...)
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  23.  20
    Real-Time Medical Image Analysis and Guidance Via Telegram Chatbot: Bridging Accessibility Gaps in Urban Healthcare.P. Shivathmika - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (10):1-12.
    Additionally, privacy and security concerns surrounding medical data are addressed by ensuring compliance with stringent data protection standards such as HIPAA and GDPR, thereby ensuring users that their medical information is handled securely and confidentially. The solution aims to improve healthcare accessibility, reduce delays in diagnosis, and provide users with greater control over their health management. This report outlines the chatbot’s design, the integration of image processing techniques, the use of machine learning models for disease identification, (...)
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  24. Advancements in AI for Medical Imaging: Transforming Diagnosis and Treatment.Zakaria K. D. Alkayyali, Ashraf M. H. Taha, Qasem M. M. Zarandah, Bassem S. Abunasser, Alaa M. Barhoom & Samy S. Abu-Naser - 2024 - International Journal of Academic Engineering Research(Ijaer) 8 (8):8-15.
    Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. (...)
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  25. Modus Tollens probabilized: deductive and Inductive Methods in medical diagnosis.Barbara Osimani - 2009 - MEDIC 17 (1/3):43-59.
    Medical diagnosis has been traditionally recognized as a privileged field of application for so called probabilistic induction. Consequently, the Bayesian theorem, which mathematically formalizes this form of inference, has been seen as the most adequate tool for quantifying the uncertainty surrounding the diagnosis by providing probabilities of different diagnostic hypotheses, given symptomatic or laboratory data. On the other side, it has also been remarked that differential diagnosis rather works by exclusion, e.g. by modus tollens, i.e. deductively. By drawing (...)
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  26.  31
    Data-Driven Insights into Chronic Kidney Disease Prediction with Machine Learning.P. Deepa - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-15.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision (...)
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  27. Dealing with elements of medical encounters: An approach based on ontological realism.Farinelli Fernanda, Almeida Mauricio, Elkin Peter & Barry Smith - 2016 - Proceedings of the Joint International Conference on Biological Ontology and Biocreative 1747.
    Electronic health records (EHRs) serve as repositories of documented data collected in a health care encounter. An EHR records information about who receives, who provides the health care and about the place where the encounter happens. We also observe additional elements relating to social relations in which the healthcare consumer is involved. To provide a consensus representation of common data and to enhance interoperability between different EHR repositories we have created a solution grounded in formal ontology. Here, we (...)
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  28. Sociocultural factors affecting first-year medical students’ adjustment to a PBL program at an African medical school.Masego Kebaetse, Dominic Griffiths, Gaonyadiwe Mokone, Mpho Mogodi, Brigid Conteh, Oathokwa Nkomazana, John Wright, Rosemary Falama & Kebaetse Maikutlo - 2024 - BMC Medical Education 24 (277):1-12.
    Background: Besides regulatory learning skills, learning also requires students to relate to their social context and negotiate it as they transition and adjust to medical training. As such, there is a need to consider and explore the role of social and cultural aspects in student learning, particularly in problem-based learning, where the learning paradigm differs from what most students have previously experienced. In this article, we report on the findings of a study exploring first-year medical students’ experiences during (...)
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  29. ANN for Predicting Medical Expenses.Khaled Salah & Ahmed Altalla - 2016 - International Journal of Engineering and Information Systems (IJEAIS) 2 (10):11-16.
    Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN (...)
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  30. Using a virtue ethics lens to develop a socially accountable community placement programme for medical students.Mpho S. Mogodi, Masego B. Kebaetse, Mmoloki C. Molwantwa, Detlef R. Prozesky & Dominic Griffiths - 2019 - BMC Medical Education 19 (246).
    Background: Community-based education (CBE) involves educating the head (cognitive), heart (affective), and the hand (practical) by utilizing tools that enable us to broaden and interrogate our value systems. This article reports on the use of virtue ethics (VE) theory for understanding the principles that create, maintain and sustain a socially accountable community placement programme for undergraduate medical students. Our research questions driving this secondary analysis were; what are the goods which are internal to the successful practice of CBE in (...)
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  31. The ethics of uncertainty for data subjects.Philip Nickel - 2019 - In Peter Dabrock, Matthias Braun & Patrik Hummel (eds.), The Ethics of Medical Data Donation. Springer Verlag. pp. 55-74.
    Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. (...)
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  32. (1 other version)Ontology-assisted database integration to support natural language processing and biomedical data-mining.Jean-Luc Verschelde, Marianna C. Santos, Tom Deray, Barry Smith & Werner Ceusters - 2004 - Journal of Integrative Bioinformatics. Repr. In: Yearbook of Bioinformatics , 39–48 1:1-10.
    Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the (...)
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  33. Public interest in health data research: laying out the conceptual groundwork.Angela Ballantyne & G. Owen Schaefer - 2020 - Journal of Medical Ethics 46 (9):610-616.
    The future of health research will be characterised by three continuing trends: rising demand for health data; increasing impracticability of obtaining specific consent for secondary research; and decreasing capacity to effectively anonymise data. In this context, governments, clinicians and the research community must demonstrate that they can be responsible stewards of health data. IRBs and RECs sit at heart of this process because in many jurisdictions they have the capacity to grant consent waivers when research is judged (...)
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  34. Harnessing Artificial Intelligence to Enhance Medical Image Analysis.Malak S. Hamad, Mohammed H. Aldeeb, Mohammed M. Almzainy, Shahd J. Albadrasawi, Musleh M. Musleh, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Health and Medical Research (IJAHMR) 8 (9):1-7.
    Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. (...)
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  35. Peculiarities of application of marketing technologies in the medical sphere.Oleksandr P. Krupskyi & Yuliya Stasiuk - 2023 - Economic Analysis 33 (3):202-212.
    Introduction. The medical sphere is constantly evolving, requiring improved approaches to its organisation and functioning. Advances in medical technology, observable changes in patient needs and growing competition challenge medical institutions to improve their strategies and approaches. Marketing technologies are becoming one of the key tools for achieving strategic goals. Purpose. This article is aimed at studying the peculiarities of the use of marketing technologies in the medical field. The main purpose of the study is to analyse (...)
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  36. The impact of national comprehensive medical reform on residents' medical expenses: Evidence from China.Changfei Nie & Yuan Feng - 2023 - Frontiers in Public Health 10:1038543.
    Residents' high medical expenses is the core challenge that needs to be solved urgently in China's medical reform for a long time. Based on the panel data of 30 provinces in Chinese Mainland during 2011–2019, we evaluate the impact of China's national comprehensive medical reform pilot policy on residents' medical expenses by using the difference-in-differences model. The results show that the pilot policy was generally conducive to reducing residents' medical expenses, resulting in a reduction (...)
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  37. The future of international marketing of higher education in Iran: A case study of the experience of Tehran University of Medical Sciences.Enayat A. Shabani - 2023 - Sjku 28 (2):134-151.
    Background and Aim: Global trends and national policies have made internationalization and paying attention to the international markets of higher education inevitable on the one hand and becoming a legal requirement of Iranian medical sciences universities on the other hand. Therefore, the main goal of this article was to show, by examining the experience of international marketing of higher education in Tehran University of Medical Sciences, what are the futures of international marketing of higher education in medical (...)
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  38. Public Preferences about Fairness and the Ethics of Allocating Scarce Medical Interventions.Govind Persad - 2017 - In Meng Li & David P. Tracer (eds.), Interdisciplinary Perspectives on Fairness, Equity, and Justice. Springer. pp. 51-65.
    This chapter examines how social- scientific research on public preferences bears on the ethical question of how those resources should in fact be allocated, and explain how social-scientific researchers might find an understanding of work in ethics useful as they design mechanisms for data collection and analysis. I proceed by first distinguishing the methodologies of social science and ethics. I then provide an overview of different approaches to the ethics of allocating scarce medical interventions, including an approach—the complete (...)
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  39. Evaluating Satisfaction Of International Students At Tehran University Of Medical Sciences (TUMS).Enayat A. Shabani - 2015 - Payavard 9 (1):97-105.
    Background and Aim: Today universities admit International Students as well as national students. Tehran University of Medical Sciences has been also started admitting International Students in regards of its Internationalization aims. Student’s satisfaction is of high importance in order to gain the given goals. The purpose of this study was to evaluate the satisfaction of International students of TUMS. -/- Materials and Methods: This was a descriptive study. The target group was international students of TUMS, the participants were selected (...)
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  40. Data over dialogue: Why artificial intelligence is unlikely to humanise medicine.Joshua Hatherley - 2024 - Dissertation, Monash University
    Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to (...)
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  41. Consent and the ethical duty to participate in health data research.Angela Ballantyne & G. Owen Schaefer - 2018 - Journal of Medical Ethics 44 (6):392-396.
    The predominant view is that a study using health data is observational research and should require individual consent unless it can be shown that gaining consent is impractical. But recent arguments have been made that citizens have an ethical obligation to share their health information for research purposes. In our view, this obligation is sufficient ground to expand the circumstances where secondary use research with identifiable health information is permitted without explicit subject consent. As such, for some studies the (...)
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  42. Embracing Reflection and Reflective Practices by Medical Professionals: A Narrative Inquiry.Priska Bastola, Bal Chandra Luitel & Binod Prasad Pant - 2024 - International Journal of Multidisciplinary Educational Research and Innovation 2 (1):33-43.
    Reflection is widely acknowledged to play a crucial role in enhancing the competence of medical professionals. Developed countries have given importance to implementing reflective practices for professional development. In developing countries, reflective practices are not given much importance as a tool for professional growth. This article aims to uncover the existing practices of reflection and the challenges faced by medical professionals working at a government hospital in Nepal. It also promotes the practice of reflection to improve daily professional (...)
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  43. Towards Industrial Strength Philosophy: How Analytical Ontology Can Help Medical Informatics.Barry Smith & Werner Ceusters - 2003 - Interdisciplinary Science Reviews 28 (2):106–111.
    Initially the problems of data integration, for example in the field of medicine, were resolved in case by case fashion. Pairs of databases were cross-calibrated by hand, rather as if one were translating from French into Hebrew. As the numbers and complexity of database systems increased, the idea arose of streamlining these efforts by constructing one single benchmark taxonomy, as it were a central switchboard, into which all of the various classification systems would need to be translated only once. (...)
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  44. Interpretable and accurate prediction models for metagenomics data.Edi Prifti, Antoine Danchin, Jean-Daniel Zucker & Eugeni Belda - 2020 - Gigascience 9 (3):giaa010.
    Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight (...)
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  45. Ontology of language, with applications to demographic data.S. Clint Dowland, Barry Smith, Matthew A. Diller, Jobst Landgrebe & William R. Hogan - 2023 - Applied ontology 18 (3):239-262.
    Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data (...)
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  46. Understanding of Authorship by the Post Graduate Medical Students at a Center in Bangladesh.S. P. Lasker - 2021 - Bangladesh Journal of Bioethics 12 (1):25-34.
    Education on authorship was delivered and evaluated by pre test and post test questionnairen on 30 post graduate medical students at the Department of Anestheology, Dhaka Medical College, Bangladesh between January and June 2019 to understand the knowledge, skill and attitude of post graduate medical students on authorship. Result: Before intervention, majority (60%) of the students felt that who perform the research work should be the author of the article. But 40% students were divided and felt that (...)
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  47.  84
    Leveraging Machine Learning Algorithms for Medical Image Classification Introduction.Ugochukwu Llodinso - manuscript
    The use of machine learning to medical image classification has seen significant development and implementation in the last several years. Computers can learn to identify patterns, make predictions, and use data to inform their judgements; this capability is known as machine learning, a branch of Artificial intelligence (AI). Classifying images according to their contents allows us to do things like identify the type of sickness, organ, or tissue depicted. Medical picture classification and interpretation using machine learning algorithms (...)
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  48. Real-Life Data of Neoadjuvant Chemotherapy in Breast Cancer: Aegean Region Experience.Atike Pınar Erdoğan, Ferhat Ekinci, Ahmet Özveren, Emine Bihter Eniseler, Bilgin Demir & Mustafa Şahbazlar - 2023 - European Journal of Therapeutics 29 (2):123-127.
    Objective: The use of neoadjuvant chemotherapy (NACT) in breast cancer is increasing. The management of locally advanced breast cancer differs due to the approach of the center to which the patient applied and the approach of the following physician. From this point of view, we aimed to evaluate the real life data of our region. -/- Methods: The study included 106 patients treated with neoadjuvant chemotherapy in the medical oncology clinic of two different university hospitals. Association between clinicopathological (...)
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  49. Nomophobia (no-mobile-phone phobia) among the undergraduate medical students.Suleman Lazarus, Abdul Rahim Ghafari, Richard Kapend, Khalid Jan Rezayee, Hasibullah Aminpoor, Mohammad Yasir Essar & Arash Nemat - 2024 - Heliyon 10 (16):1-13.
    Nomophobia (no-mobile-phone phobia) is the fear and anxiety of being without a mobile phone. This study pioneers the investigation of nomophobia in Afghanistan using the Nomophobia Questionnaire (NMP-Q), addressing a crucial gap in the field. We collected statistical data from 754 undergraduate medical students, comprising men (56.50 %) and women (43.50 %), and analyzed the dimensions of nomophobia. While results revealed that all but two participants were nomophobic, they identified three significant dimensions affecting the level of nomophobia among (...)
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  50. Artificial Intelligence in Healthcare: Transforming Patient Care and Medical Practices.Jawad Y. I. Alzamily, Hani Bakeer, Husam Almadhoun, Basem S. Abunasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Engineering Research (IJAER) 8 (8):1-9.
    Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to (...)
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