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  1.  31
    Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship.Florian Funer - 2022 - Philosophy and Technology 35 (1):1-20.
    The initial successes in recent years in harnessing machine learning technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as (...)
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  2.  14
    The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach.Florian Funer - 2022 - Medicine, Health Care and Philosophy 25 (2):167-178.
    Developments in Machine Learning (ML) have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and (...)
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  3.  11
    Admitting the heterogeneity of social inequalities: intersectionality as a (self-)critical framework and tool within mental health care.Florian Funer - 2023 - Philosophy, Ethics, and Humanities in Medicine 18 (1):1-9.
    Inequities shape the everyday experiences and life chances of individuals at the margins of societies and are often associated with lower health and particular challenges in accessing quality treatment and support. This fact is even more dramatic for those individuals who live at the nexus of different marginalized groups and thus may face multiple discrimination, stigma, and oppression. To address these multiple social and structural disadvantages, intersectional approaches have recently gained a foothold, especially in the public health field. This study (...)
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  4.  14
    Responsibility and decision-making authority in using clinical decision support systems: an empirical-ethical exploration of German prospective professionals’ preferences and concerns.Florian Funer, Wenke Liedtke, Sara Tinnemeyer, Andrea Diana Klausen, Diana Schneider, Helena U. Zacharias, Martin Langanke & Sabine Salloch - 2024 - Journal of Medical Ethics 50 (1):6-11.
    Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals’ attitudes to potential changes of responsibility and decision-making (...)
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  5.  16
    Rahmenbedingungen einer Forschungsethik der datenintensiven medizinischen Forschung.Urban Wiesing & Florian Funer - forthcoming - Ethik in der Medizin:1-14.
    Zusammenfassung Die Forschungs- und Regulierungsebene bei datenintensiver Forschung in der Medizin liegen auseinander. Ein heterogenes Feld aus regulierenden Institutionen mit regional ungleichen Regelungen, sowohl hinsichtlich der Dichte als auch der Restriktivität von Regelungen, steht einer globalen Entwicklung der Technologien entgegen. Trotz oder gerade wegen mangelnder global-gültiger Regulierungen können auch unverbindliche oder nur bedingt verbindliche normative Vorgaben der Orientierung dienen. Doch wie soll eine solche normative Regulierung angesichts datenintensiver Forschung in der Medizin ausgestaltet werden und woran soll sie sich orientieren? Die (...)
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  6.  12
    On the way to the digital homo vitruvianus? Medical self-tracking and digital health applications (DiGA) between empowerment and loss of control.Florian Funer - 2021 - Ethik in der Medizin 33 (1):13-30.
    Definition of the problemHealth Apps are becoming increasingly important for a preventive and responsible orientation of the health system. Currently, most of these digital health applications (DiGA) are based on so-called self-tracking technologies which record physiological and psychological data via sensors, usually combined with personalized everyday information. In the last few years, these digital developments have launched an intense and clearly polarized debate about the opportunities and dangers of self-tracking in healthcare.ArgumentsAfter a brief overview of medical self-tracking, this essay wants (...)
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  7.  14
    Auf dem Weg zum digitalen homo vitruvianus? Medizinisches Selftracking und digitale Gesundheitsanwendungen (DiGA) zwischen Empowerment und Kontrollverlust.Florian Funer - 2020 - Ethik in der Medizin 33 (1):13-30.
    Zunehmend gewinnen Health Apps an Bedeutung für eine präventive und eigenverantwortliche Ausrichtung des Gesundheitssystems. Die meisten dieser digitalen Gesundheitsanwendungen basieren derzeit auf sog. Selftracking-Technologien, mit deren Hilfe physiologische und psychische Daten sensorgestützt aufgezeichnet und diese zumeist um personalisierte Alltagsinformationen ergänzt werden. Die digitalen Entwicklungen dieser Art lösten in den letzten Jahren eine intensive und deutlich polarisierte Debatte über die Chancen und Gefahren von gesundheitlichem Selftracking aus. Ziel dieser Arbeit ist es, nach einem kurzen Überblick über das Feld des medizinischen Selftracking (...)
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  8.  6
    ‘Can I trust my patient?’ Machine Learning support for predicting patient behaviour.Florian Funer & Sabine Salloch - 2023 - Journal of Medical Ethics 49 (8):543-544.
    Giorgia Pozzi’s feature article1 on the risks of testimonial injustice when using automated prediction drug monitoring programmes (PDMPs) turns the spotlight on a pressing and well-known clinical problem: physicians’ challenges to predict patient behaviour, so that treatment decisions can be made based on this information, despite any fallibility. Currently, as one possible way to improve prognostic assessments of patient behaviour, Machine Learning-driven clinical decision support systems (ML-CDSS) are being developed and deployed. To make her point, Pozzi discusses ML-CDSSs that are (...)
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  9.  8
    Digitalisierung, Daten und KI in Medizin und Pflege. Virtuelles Nachwuchskolloquium des „Netzwerks Junge Medizinethik“.Philipp Karschuck, Svenja Wiertz, Frank Ursin, Wenke Liedtke, Kris Vera Hartmann & Florian Funer - 2021 - Ethik in der Medizin 33 (3):415-420.
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