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Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them
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 informa...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376350/ https://www.ncbi.nlm.nih.gov/pubmed/35978652 http://dx.doi.org/10.3389/frai.2022.913093 |
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author | von Ulmenstein, Ulrich Tretter, Max Ehrlich, David B. Lauppert von Peharnik, Christina |
author_facet | von Ulmenstein, Ulrich Tretter, Max Ehrlich, David B. Lauppert von Peharnik, Christina |
author_sort | von Ulmenstein, Ulrich |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9376350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93763502022-08-16 Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them von Ulmenstein, Ulrich Tretter, Max Ehrlich, David B. Lauppert von Peharnik, Christina Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2022-08-01 /pmc/articles/PMC9376350/ /pubmed/35978652 http://dx.doi.org/10.3389/frai.2022.913093 Text en Copyright © 2022 von Ulmenstein, Tretter, Ehrlich and Lauppert von Peharnik. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence von Ulmenstein, Ulrich Tretter, Max Ehrlich, David B. Lauppert von Peharnik, Christina Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
title | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
title_full | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
title_fullStr | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
title_full_unstemmed | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
title_short | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
title_sort | limiting medical certainties? funding challenges for german and comparable public healthcare systems due to ai prediction and how to address them |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376350/ https://www.ncbi.nlm.nih.gov/pubmed/35978652 http://dx.doi.org/10.3389/frai.2022.913093 |
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