Personalized HIV Treatment: Bringing Marginalized Patients to the Forefront With Situational Analysis


  • Renate Baumgartner Eberhard Karls Universität Tübingen



personalized medicine, antiretroviral therapy, health care, artificial intelligence, machine learning, implicated actors


Since the early 2000s, personalized medicine (PM) has been a much-hyped field of healthcare. HIV treatment optimization tools were one of the first successful examples of PM, and have since their development been used to find tailored and optimized treatment for HIV-positive people. In this paper on a case study of the social arena of personalized HIV therapy I show how social worlds worked on both shared and distinct goals within the arena. I highlight the simultaneous centering and marginalization to which people seeking HIV therapy were subjected discursively in the social worlds. I also demonstrate that the further patients were from practitioners' daily work, the more they were reduced to their blood samples, rather than being constructed as complex and human.



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Author Biography

Renate Baumgartner, Eberhard Karls Universität Tübingen

Renate BAUMGARTNER works as post-doctoral researcher at the Center of Gender and Diversity Research at the University of Tübingen. She conducts research in (feminist) science and technology studies, especially on AI in healthcare and medicine.


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How to Cite

Baumgartner, R. (2023). Personalized HIV Treatment: Bringing Marginalized Patients to the Forefront With Situational Analysis. Forum Qualitative Sozialforschung Forum: Qualitative Social Research, 24(2).



Social Worlds, Arenas, and Situational Analyses: Theoretical Debates and Practical Research Experiences