Identifying opportunities for AI applications in healthcare — Renewing the national healthcare and social services

Abstract

Abstract-A vast variety of artificial intelligence techniques have been deployed to specific healthcare problems during the last thirty years with varying levels of success while there is a shortage of systematic matching of AI capabilities with the breadth of application opportunities. In this paper, we describe the process of identifying opportunities for deploying artificial intelligence to healthcare and social services on regional and national levels in Finland. The project involved a large number of stakeholders from a variety of backgrounds ranging from governmental agencies to entrepreneurs. The process described includes idea generation of an application or solution and its elaboration in workshops using a design thinking method. The resulting idea pool was filtered down to 34 best use case descriptions, which went through an architectural design process identifying AI capabilities needed in the components of these designs reported in this paper. The potential ones of the use cases were selected for prototype development. The subsequent steps in the process include feasibility prototypes and evaluation of the economic and business value of the solutions and applications.

The Ethics of Digital Well-Being: A Thematic Review

“…They range from the risks that the exposure of health-related information may pose for an individual's well-being due to real and perceived stigmatization (Mittelstadt 2017a), to concerns about the inference of sensitive information from seemingly benign (and in some cases public) datasets (Horvitz and Mulligan 2015). The risks are most prominent in relation to smart home technologies, either in a patient's own home or in a care home setting, where expectations of privacy are often highest (Feng et al 2018;Margot-Cattin and Nygård 2009;Mulvenna et al 2017;Palm 2013;Tyrväinen et al 2018).…”

The Ethics of Digital Well-Being: A Thematic Review

“…They range from the risks that the exposure of health-related information may pose for an individual's well-being due to real and perceived stigmatization (Mittelstadt 2017b), to concerns about the inference of sensitive information from seemingly benign (and in some cases public) datasets (Horvitz & Mulligan, 2015). The risks are most prominent in relation to smart home technologies, either in a patient's own home or in a care home setting, where expectations of privacy are often highest (Feng et al, 2018;Margot-Cattin & Nygård, 2009;Mulvenna et al, 2017;Palm, 2013;Tyrväinen et al, 2018).…”

Adversarial Attacks Against Audio Surveillance Systems

“…Combined with the limited interpretability of many current deep nets [5], adversarial attacks contribute to the uncertainty surrounding AI when it comes to real-world critical applications (e.g. healthcare [6], critical infrastructures [7], to mention but a few) and especially its trustworthiness [8].…”