Michal Frumer

Michal Frumer serves as a Part-time Lecturer at Aarhus University's Department of Public Health and Researcher at the Interacting Minds Centre (School of Culture and Society). Holding a PhD and cand.scient.anth degree in Anthropology, she bridges medical anthropology with clinical diagnostics in Danish healthcare settings.

Education:

• PhD in Anthropology (Aarhus University)
• cand.scient.anth (Master of Science in Anthropology)

Frumer's research centers on the anthropology of diagnostic uncertainty, examining how anticipation, knowledge-practices, and ethical dilemmas manifest in cancer care and AI-driven diagnostics. Her ethnographic work explores the lived experiences of patients under surveillance for conditions like pulmonary nodules, revealing how diagnostic technologies reshape everyday life, social relations, and ethical decision-making. She critically analyzes the interplay between biomedical expertise, patient agency, and institutional priorities within Denmark's healthcare system, with growing focus on AI integration in image-based diagnostics and sepsis algorithms.

Analysis of her publication trajectory shows consistent exploration of temporal dimensions in medical uncertainty, semiotic challenges in diagnostic interpretation, and infrastructural responses to ambiguity in cancer care. Her work demonstrates methodological rigor in qualitative approaches while addressing urgent questions about technology adoption in high-stakes clinical environments.

Research Projects:

• Practicing uncertainty: trying out the potential of lung cancer (2015-2017): Ethnographic investigation of lung cancer diagnostic potential through fieldwork
• Et antropologisk studie af præ-kræft diagnostik (2017-2020): Study of pre-cancer diagnostics focusing on prioritization frameworks and patient needs

Frumer actively contributes to the Interacting Minds Centre's interdisciplinary mission while maintaining hospital-based research at Horsens Regional Hospital. Her current work examines AI implementation challenges in clinical diagnostics, emphasizing human-technology interaction in time-critical medical contexts.