Results for 'Medical AI '

957 found
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  1. Medical AI: is trust really the issue?Jakob Thrane Mainz - 2024 - Journal of Medical Ethics 50 (5):349-350.
    I discuss an influential argument put forward by Hatherley in theJournal of Medical Ethics. Drawing on influential philosophical accounts of interpersonal trust, Hatherley claims that medical artificial intelligence is capable of being reliable, but not trustworthy. Furthermore, Hatherley argues that trust generates moral obligations on behalf of the trustee. For instance, when a patient trusts a clinician, it generates certain moral obligations on behalf of the clinician for her to do what she is entrusted to do. I make (...)
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  2. Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts.Paul Formosa, Wendy Rogers, Yannick Griep, Sarah Bankins & Deborah Richards - 2022 - Computers in Human Behaviour 133.
    Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...)
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  3. Limits of trust in medical AI.Joshua James Hatherley - 2020 - Journal of Medical Ethics 46 (7):478-481.
    Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI (...)
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  4. The virtues of interpretable medical AI.Joshua Hatherley, Robert Sparrow & Mark Howard - forthcoming - Cambridge Quarterly of Healthcare Ethics.
    Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value of (...)
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  5. Medical AI, Inductive Risk, and the Communication of Uncertainty: The Case of Disorders of Consciousness.Jonathan Birch - forthcoming - Journal of Medical Ethics.
    Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a procedure (...)
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  6.  34
    The Role of Sympathy in Critical Reasoning and the Limitations of Current Medical AI.Martina Favaretto & Kyle Stroh - forthcoming - Journal of Medicine and Philosophy.
    The recent developments of medical AI systems (MAIS) open up questions as to whether and to what extent MAIS can be modeled to include empathetic understanding, as well as what impact MAIS’ lack of empathetic understanding would have on its ability to perform the necessary critical analyses for reaching a diagnosis and recommending medical treatment. In this paper, we argue that current medical AI systems’ ability to empathize with patients is severely limited due to its lack of (...)
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  7. Two Reasons for Subjecting Medical AI Systems to Lower Standards than Humans.Jakob Mainz, Jens Christian Bjerring & Lauritz Munch - 2023 - Acm Proceedings of Fairness, Accountability, and Transaparency (Facct) 2023 1 (1):44-49.
    This paper concerns the double standard debate in the ethics of AI literature. This debate essentially revolves around the question of whether we should subject AI systems to different normative standards than humans. So far, the debate has centered around the desideratum of transparency. That is, the debate has focused on whether AI systems must be more transparent than humans in their decision-making processes in order for it to be morally permissible to use such systems. Some have argued that the (...)
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  8. Black-box assisted medical decisions: AI power vs. ethical physician care.Berman Chan - 2023 - Medicine, Health Care and Philosophy 26 (3):285-292.
    Without doctors being able to explain medical decisions to patients, I argue their use of black box AIs would erode the effective and respectful care they provide patients. In addition, I argue that physicians should use AI black boxes only for patients in dire straits, or when physicians use AI as a “co-pilot” (analogous to a spellchecker) but can independently confirm its accuracy. I respond to A.J. London’s objection that physicians already prescribe some drugs without knowing why they work.
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  9. AI-Based Medical Solutions Can Threaten Physicians’ Ethical Obligations Only If Allowed to Do So.Benjamin Gregg - 2023 - American Journal of Bioethics 23 (9):84-86.
    Mildred Cho and Nicole Martinez-Martin (2023) distinguish between two of the ways in which humans can be represented in medical contexts. One is technical: a digital model of aspects of a person’s...
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  10. “Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocation.Jon Rueda, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín & David Rodríguez-Arias - 2022 - AI and Society:1-12.
    The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps (...)
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  11.  31
    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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  12. Responsible nudging for social good: new healthcare skills for AI-driven digital personal assistants.Marianna Capasso & Steven Umbrello - 2022 - Medicine, Health Care and Philosophy 25 (1):11-22.
    Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and (...)
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  13. High hopes for “Deep Medicine”? AI, economics, and the future of care.Robert Sparrow & Joshua Hatherley - 2020 - Hastings Center Report 50 (1):14-17.
    In Deep Medicine, Eric Topol argues that the development of artificial intelligence (AI) for healthcare will lead to a dramatic shift in the culture and practice of medicine. Topol claims that, rather than replacing physicians, AI could function alongside of them in order to allow them to devote more of their time to face-to-face patient care. Unfortunately, these high hopes for AI-enhanced medicine fail to appreciate a number of factors that, we believe, suggest a radically different picture for the future (...)
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  14. The virtues of interpretable medical artificial intelligence.Joshua Hatherley, Robert Sparrow & Mark Howard - forthcoming - Cambridge Quarterly of Healthcare Ethics.
    Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value of (...)
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  15. Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?Frank Ursin, Felix Lindner, Timo Ropinski, Sabine Salloch & Cristian Timmermann - 2023 - Ethik in der Medizin 35 (2):173-199.
    Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the (...)
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  16. AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.
    Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive (...)
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  17. Trust in Medical Artificial Intelligence: A Discretionary Account.Philip J. Nickel - 2022 - Ethics and Information Technology 24 (1):1-10.
    This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by (...)
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  18. Using Edge Cases to Disentangle Fairness and Solidarity in AI Ethics.James Brusseau - 2021 - AI and Ethics.
    Principles of fairness and solidarity in AI ethics regularly overlap, creating obscurity in practice: acting in accordance with one can appear indistinguishable from deciding according to the rules of the other. However, there exist irregular cases where the two concepts split, and so reveal their disparate meanings and uses. This paper explores two cases in AI medical ethics – one that is irregular and the other more conventional – to fully distinguish fairness and solidarity. Then the distinction is applied (...)
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  19. How to Use AI Ethically for Ethical Decision-Making.Joanna Demaree-Cotton, Brian D. Earp & Julian Savulescu - 2022 - American Journal of Bioethics 22 (7):1-3.
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  20. Could You Merge With AI? Reflections on the Singularity and Radical Brain Enhancement.Cody Turner & Susan Schneider - 2020 - In Markus Dirk Dubber, Frank Pasquale & Sunit Das (eds.), The Oxford Handbook of Ethics of Ai. Oxford Handbooks. pp. 307-325.
    This chapter focuses on AI-based cognitive and perceptual enhancements. AI-based brain enhancements are already under development, and they may become commonplace over the next 30–50 years. We raise doubts concerning whether radical AI-based enhancements transhumanists advocate will accomplish the transhumanists goals of longevity, human flourishing, and intelligence enhancement. We urge that even if the technologies are medically safe and are not used as tools by surveillance capitalism or an authoritarian dictatorship, these enhancements may still fail to do their job for (...)
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  21.  19
    Towards a Taxonomy of AI Risks in the Health Domain.Delaram Golpayegani, Joshua Hovsha, Leon Rossmaier, Rana Saniei & Jana Misic - 2022 - 2022 Fourth International Conference on Transdisciplinary Ai (Transai).
    The adoption of AI in the health sector has its share of benefits and harms to various stakeholder groups and entities. There are critical risks involved in using AI systems in the health domain; risks that can have severe, irreversible, and life-changing impacts on people’s lives. With the development of innovative AI-based applications in the medical and healthcare sectors, new types of risks emerge. To benefit from novel AI applications in this domain, the risks need to be managed in (...)
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  22. Health Care Using AI.T. Poongodi - 2019 - International Journal of Research and Analytical Reviews 6 (2):141-145.
    Breast cancer treatment is being transformed by artificial intelligence (AI). Nevertheless, most scientists, engineers, and physicians aren't ready to contribute to the healthcare AI revolution. In this paper, we discuss our experiences teaching a new American student undergraduate course that seeks to train the next generation for cross-cultural design thinking, which we believe is critical for AI to realize its full potential in breast cancer treatment. The main tasks of this course are preparing, performing and translating interviews with healthcare professionals (...)
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  23. First human upload as AI Nanny.Alexey Turchin - manuscript
    Abstract: As there are no visible ways to create safe self-improving superintelligence, but it is looming, we probably need temporary ways to prevent its creation. The only way to prevent it, is to create special AI, which is able to control and monitor all places in the world. The idea has been suggested by Goertzel in form of AI Nanny, but his Nanny is still superintelligent and not easy to control, as was shown by Bensinger at al. We explore here (...)
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  24. The Prospects of Using AI in Euthanasia and Physician-Assisted Suicide: A Legal Exploration.Hannah van Kolfschooten - 2024 - AI and Ethics 1.
    The Netherlands was the first country to legalize euthanasia and physician-assisted suicide. This paper offers a first legal perspective on the prospects of using AI in the Dutch practice of euthanasia and physician-assisted suicide. It responds to the Regional Euthanasia Review Committees’ interest in exploring technological solutions to improve current procedures. The specific characteristics of AI – the capability to process enormous amounts of data in a short amount of time and generate new insights in individual cases – may for (...)
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  25. 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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  26. On algorithmic fairness in medical practice.Thomas Grote & Geoff Keeling - 2022 - Cambridge Quarterly of Healthcare Ethics 31 (1):83-94.
    The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness (...)
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  27. The promise and perils of AI in medicine.Robert Sparrow & Joshua James Hatherley - 2019 - International Journal of Chinese and Comparative Philosophy of Medicine 17 (2):79-109.
    What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It’s also highly likely to impact on the organisational and business practices (...)
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  28.  45
    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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  29. HARMONIZING LAW AND INNOVATIONS IN NANOMEDICINE, ARTIFICIAL INTELLIGENCE (AI) AND BIOMEDICAL ROBOTICS: A CENTRAL ASIAN PERSPECTIVE.Ammar Younas & Tegizbekova Zhyldyz Chynarbekovna - manuscript
    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.
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  30. Augmented Intelligence - The New AI - Unleashing Human Capabilities in Knowledge Work.James M. Corrigan - 2012 - 2012 34Th International Conference on Software Engineering (Icse 2012).
    In this paper I describe a novel application of contemplative techniques to software engineering with the goal of augmenting the intellectual capabilities of knowledge workers within the field in four areas: flexibility, attention, creativity, and trust. The augmentation of software engineers’ intellectual capabilities is proposed as a third complement to the traditional focus of methodologies on the process and environmental factors of the software development endeavor. I argue that these capabilities have been shown to be open to improvement through the (...)
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  31.  63
    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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  32. Riscrivere la filosofia della natura di Alberto Magno nel XIV secolo. Il V libro della Catena aurea entium di Enrico di Herford e il commento di Alberto ai Meteorologica di Aristotele.Chiara Marcon - 2024 - Noctua 11 (1):1-48.
    The Catena aurea entium of Henry of Herford is part of the work of re-elaboration of Aristotle’s natural-philosophical corpus, which characterised the European intellectual environment in the Late Middle Ages. In the central books of his encyclopaedia, Henry comments on the works of natural philosophy of Albert the Great, placing himself in continuity with the cultural project started by Albert in Cologne. The present article aims to compare the 5th book of the Catena aurea entium, which consists of a comment (...)
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  33. The Unobserved Anatomy: Negotiating the Plausibility of AI-Based Reconstructions of Missing Brain Structures in Clinical MRI Scans.Paula Muhr - 2023 - In Antje Flüchter, Birte Förster, Britta Hochkirchen & Silke Schwandt (eds.), Plausibilisierung und Evidenz: Dynamiken und Praktiken von der Antike bis zur Gegenwart. Bielefeld University Press. pp. 169-192.
    Vast archives of fragmentary structural brain scans that are routinely acquired in medical clinics for diagnostic purposes have so far been considered to be unusable for neuroscientific research. Yet, recent studies have proposed that by deploying machine learning algorithms to fill in the missing anatomy, clinical scans could, in future, be used by researchers to gain new insights into various brain disorders. This chapter focuses on a study published in2019, whose authors developed a novel unsupervised machine learning algorithm for (...)
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  34. Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary?Frank Ursin, Cristian Timmermann & Florian Steger - 2022 - Bioethics 36 (2):143-153.
    Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical (...)
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  35. Why algorithmic speed can be more important than algorithmic accuracy.Jakob Mainz, Lauritz Munch, Jens Christian Bjerring & Sissel Godtfredsen - 2023 - Clinical Ethics 18 (2):161-164.
    Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.
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  36. Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.
    Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their (...)
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  37.  74
    Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?Frank Ursin, Cristian Timmermann, Marcin Orzechowski & Florian Steger - 2021 - Frontiers in Medicine 8:695217.
    Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. -/- Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a (...)
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  38. Artificial Intelligence in Life Extension: from Deep Learning to Superintelligence.Alexey Turchin, Denkenberger David, Zhila Alice, Markov Sergey & Batin Mikhail - 2017 - Informatica 41:401.
    In this paper, we focus on the most efficacious AI applications for life extension and anti-aging at three expected stages of AI development: narrow AI, AGI and superintelligence. First, we overview the existing research and commercial work performed by a select number of startups and academic projects. We find that at the current stage of “narrow” AI, the most promising areas for life extension are geroprotector-combination discovery, detection of aging biomarkers, and personalized anti-aging therapy. These advances could help currently living (...)
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  39. Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?Alex John London - 2022 - Cell Reports Medicine 100622 (3):1-8.
    There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that structure their development. (...)
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  40. May Artificial Intelligence take health and sustainability on a honeymoon? Towards green technologies for multidimensional health and environmental justice.Cristian Moyano-Fernández, Jon Rueda, Janet Delgado & Txetxu Ausín - 2024 - Global Bioethics 35 (1).
    The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon (...)
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  41. A generic model of consciousness.Mark J. Hadley - 2023 - Journal of Artificial Intelligence and Consciousness 10 (2):291--308.
    This is a model of consciousness. The hard problem of consciousness, what it feels like, is answered. The work builds on medical research analyzing the source and mechanisms associated with our feelings. It goes further by describing a generic model with wide applicability. The model is fully consistent with medical pathways in humans, but easily extends to animals and AI. The essence of the model is the interplay between associative memory and physiology. The model is a clear and (...)
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  42.  29
    Algorithms Advise, Humans Decide: the Evidential Role of the Patient Preference Predictor.Nicholas Makins - forthcoming - Journal of Medical Ethics.
    An AI-based “patient preference predictor” (PPP) is a proposed method for guiding healthcare decisions for patients who lack decision-making capacity. The proposal is to use correlations between sociodemographic data and known healthcare preferences to construct a model that predicts the unknown preferences of a particular patient. In this paper, I highlight a distinction that has been largely overlooked so far in debates about the PPP–that between algorithmic prediction and decision-making–and argue that much of the recent philosophical disagreement stems from this (...)
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  43.  39
    (1 other version)Institutional Trust in Medicine in the Age of Artificial Intelligence.Michał Klincewicz - 2023 - In David Collins, Iris Vidmar Jovanović, Mark Alfano & Hale Demir-Doğuoğlu (eds.), The Moral Psychology of Trust. Lexington Books.
    It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role (...)
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  44. What Does It Mean to Be Human Today?Julia Alessandra Harzheim - forthcoming - Cambridge Quarterly of Healthcare Ethics.
    With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves “in the image of our machines,” and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against this (...)
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  45. Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Joshua Hatherley & Robert Sparrow - 2023 - Journal of the American Medical Informatics Association 30 (2):361-366.
    Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial are (...)
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  46. Biomedical Ontologies.Barry Smith - 2022 - In Peter L. Elkin (ed.), Terminology, Ontology and Their Implementations: Teaching Guide and Notes. Springer. pp. 125-169.
    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 (...)
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  47. Care Depersonalized: The Risk of Infocratic “Personalised” Care and a Posthuman Dystopia.Matthew Tieu & Alison L. Kitson - 2023 - American Journal of Bioethics 23 (9):89-91.
    Much of the discussion of the role of emerging technologies associated with AI, machine learning, digital simulacra, and relevant ethical considerations such as those discussed in the target article, take a relatively narrow and episodic view of a person’s healthcare needs. There is much speculation about diagnostic, treatment, and predictive applications but relatively little consideration of how such technologies might be used to address a person’s lived experience of illness and ongoing care needs. This is likely due to the greater (...)
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  48. CLIPS - Expert System to Predict Coriander Diseases.Y. I. Aslem & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (6):89-95.
    Artificial intelligence is one of the most rapidly evolving fields today. It is used in the majority of computer science applications nowadays. Expert systems are one of the most valuable types of AI; they are used to deliver predictions and decisions in order to make scientific, medical, and even architectural challenges easier to address; they will eventually take the place of a human expert. Without having to meet with a genuine human specialist, the user will be able to get (...)
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  49. A Proposed Expert System for Vertigo Diseases Diagnosis.Dina F. Al-Borno & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (6):1-9.
    Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, (...)
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  50. Olfactory Virtual Reality (OVR) for Wellbeing and Reduction of Stress, Anxiety and Pain.David Tomasi - 2021 - Journal of Medical Research and Health Sciences 4 (3).
    Olfactory Virtual Reality (OVR) for Wellbeing and Reduction of Stress, Anxiety and Pain - Journal of Medical Research and Health Sciences.
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