Healthcare uses of artificial intelligence: Challenges and opportunities for growth

Abstract

Forms of Artificial Intelligence (AI), like deep learning algorithms and neural networks, are being intensely explored for novel healthcare applications in areas such as imaging and diagnoses, risk analysis, lifestyle management and monitoring, health information management, and virtual health assistance. Expected benefits in these areas are wide-ranging and include increased speed in imaging, greater insight into predictive screening, and decreased healthcare costs and inefficiency. However, AI-based clinical tools also create a host of situations wherein commonly-held values and ethical principles may be challenged. In this short column, we highlight three potentially problematic aspects of AI use in healthcare: (1) dynamic information and consent, (2) transparency and ownership, and (3) privacy and discrimination. We discuss their impact on patient/client, clinician, and health institution values and suggest ways to tackle this impact. We propose that AI-related ethical challenges may represent an opportunity for growth in organizations.

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

“…Starting once again with the individual LoA: as more diagnostic and therapeutic interventions become based on AI-Health solutions, individuals may be encouraged to share more and more personal data about themselves (Racine et al, 2019) -data that can then be used in opaque ways (Sterckx et al, 2016). This means that the ability for individuals to be meaningfully involved in shared decision making is considerably undermined As a result, the increasing use of algorithmic decision-making in clinical settings can have negative implications for individual autonomy, as for an individual to be able to exert agency over the AI-Health derived clinical decision, they would need to have a good understanding of the underlying data, processes and technical possibilities that were involved in it being reached (DuFault & Schouten, 2018) and be able to ensure their own values are taken into consideration (McDougall, 2019).…”

Translational health technology and system schemes: enhancing the dynamics of health informatics

“…Forms of Artificial Intelligence (AI) like deep learning algorithms and neural networks are explored for novel healthcare applications in areas like imaging and diagnostics, treatment, risk analysis, health information management, virtual assistance, and patient monitoring [45,46]. In addition to advanced mathematical models, the implementation of AI can be perceived as AI-based software that informs or influences clinical or administrative decisions and healthcare delivery [47].…”

“…Such evaluation should not focus only on the technical properties of AI but moreover on the challenges of using AI in clinical practice [ 47 ]. Fulfilling the potential of AI: “better care at lower costs” requires AI-related best practices and understanding connected ethical challenges as well [ 46 , 55 ].…”

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

“…If a decision made by clinical decision support software leads to a negative outcome for the individual, it is unclear who to assign the responsibility and or liability to and therefore to prevent it from happening again (Racine, Boehlen, & Sample, 2019).. 3 A level of abstraction can be imagined as an interface that enables one to observe some aspects of a system analysed, while making other aspects opaque or indeed invisible. For example, one may analyse a house at the LoA of a buyer, of an architect, of a city planner, of a plumber, and so on.…”

“…Starting once again with the individual LoA: as more diagnostic and therapeutic interventions become based on AI-Health solutions, individuals may be encouraged to share more and more personal data about themselves (Racine et al, 2019) -data that can then be used in opaque ways (Sterckx et al, 2016). This means that the ability for individuals to be meaningfully involved in shared decision making is considerably undermined As a result, the increasing use of It is not necessarily the case that harmful impacts will primarily be felt by the patients.…”