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Institute of Biomedical Ethics and History of Medicine (IBME)

Bjørn Hofmann

Bjørn Hofmann, Prof. Dr.

  • Stehr Boldt Fellow

Short Bio

Bjørn Hofmann is a Norwegian professor in philosophy of medicine and bioethics with special interest for the relationship between epistemology and ethics. He is affiliated with and the Centre for Medical Ethics at the University of Oslo in Norway and the Department of Health Science at the Norwegian University of Science and Technology (NTNU) at Gjøvik. Hofmann is trained in the natural sciences (electrical engineering and biomedical technology), history of ideas, and philosophy. His main subjects and interests are basic concepts for health care, norms of knowledge and evidence production, the theory and practice of governing technology, health services research, and (bio)medical ethics

Research Interest

  • Basic concepts for health care: disease, (f)utility, causality, (dys)function, (over)diagnosis, medicalization, naturalness, enhancement, severity
  • Norms of knowledge: knowledge generation, evidence production, norms of science (science ethics, research ethics, research integrity), forms of rationality
  • Handling of technology: Health Technology Assessment (HTA), technology development (emergent/disruptive technology), ethics of technology (technological norms, value-ladenness), biases in handling technologies
  • (bio)Medical ethics: Clinical ethics (surgery, ART, tx), autonomy, consent, shared decision-making, justice, priority setting, diagnostic dilemmas, incidental findings, screening, biobanks, health registries, AI/ML in healthcare

Ongoing Projects

  • The ethics of biotechnology
  • Ethics in Health Technology Assessment (HTA)
  • Forming epistemic and moral norms in medicine and health care
  • Rationality in medicine (inferences, causality, biases, imperatives,heuristics)
  • Ethical and epistemological aspects of organoid Research
  • Medical Uncertainty
  • On the concept of disease
  • On the person in personal health responsibility
  • Overdiagnosis
  • Responsible Explainable Machine Learning for Sleep-related Respiratory Disorders
  • Scientific misconduct