Cyber Security and Securing Subjective Patient Quality Engagements in Medical Applications: AI and Vulnerabilities

Public policy arguments about the appropriate valuation of life in public policy have generally moved on from accounting resource costs to recognizing and including revealed preference valuations. In medical and public health, the valuation of the “quality” life experience over time (a very different concept) has focused on equivalents to an unconstrained life experience. The user perspective on these valuations is largely missing and has substantial implications for both individuals and organizations. As these summary measures are fundamental to current triage and assessment processes, they have considerable importance for individuals. Recognizing this, requires consideration of the number of different channels that can bias the assessment of individuals in specific circumstances when AI support is used, as a wide range of inferences from diverse data are then brought to bear and amplifies health data security scope and importance. These intermediate aggregate channel security vulnerabilities have not previously been highlighted, in spite of the importance attached to these summary measures in public health. The collision of totally individualized assumed impacts and end-user consequences are amplified rapidly by AI methods in support of decisions, and the security and privacy-if any is left—of individual medical AND life data are thus jointly at cyber risk. This article is a preliminary—largely gedanken- exploration of the results of combining these very different technical views and perspectives, and is intended to stimulate work that takes all these aspects into account.