Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them

von Ulmenstein U, Tretter M, Ehrlich DB, Lauppert von Peharnik C (2023)

Publication Language: English

Publication Type: Book chapter / Article in edited volumes

Publication year: 2023

Publisher: Frontiers Media

Edited Volumes: AI and Healthcare Financial Management (HFM). Towards Sustainable Development

Series: Frontiers in Artificial Intelligence Research Topics

City/Town: Lausanne

Pages Range: 109–122

ISBN: 978-2-8325-1075-9

URI: https://www.frontiersin.org/research-topics/30170/ai-and-healthcare-financial-management-hfm-towards-sustainable-development

DOI: 10.3389/978-2-8325-1075-9

Open Access Link: https://www.frontiersin.org/research-topics/30170/ai-and-healthcare-financial-management-hfm-towards-sustainable-development

Abstract

Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.

Authors with CRIS profile

Max Tretter Lehrstuhl für Systematische Theologie II (Ethik)

Related research project(s)

CwiC: Coping with Certainty – von der Steigerung der Prädiktabilität im KI-Zeitalter und ihren damit einhergehenden normativen Herausforderungen (CwiC) March 1, 2020 - Feb. 28, 2023

Involved external institutions

Justus-Liebig-Universität Gießen DE Germany (DE) Karlsruhe Institute of Technology (KIT) DE Germany (DE)

How to cite

APA:

von Ulmenstein, U., Tretter, M., Ehrlich, D.B., & Lauppert von Peharnik, C. (2023). Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them. In Ananth Rao, Immanuel Azaad Moonesar, Arkalgud Ramaprasad et al. (Eds.), AI and Healthcare Financial Management (HFM). Towards Sustainable Development. (pp. 109–122). Lausanne: Frontiers Media.

MLA:

von Ulmenstein, Ulrich, et al. "Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them." AI and Healthcare Financial Management (HFM). Towards Sustainable Development. Ed. Ananth Rao, Immanuel Azaad Moonesar, Arkalgud Ramaprasad et al., Lausanne: Frontiers Media, 2023. 109–122.

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