AI-Assisted Decision-making in Healthcare

Abstract

Artificial intelligence (AI) is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019). The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. (2019) in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper.

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

“…Our findings show that most used methods of CDSSs are analysis or comparison of genetic and phenotypic data, followed by information retrieval and machine learning. However, we could not identify many publications considering machine learning, although it plays an increasing role in healthcare [ 45 ]. In other medical fields, a higher number of CDSSs can be found.…”

An Ethics Framework for Big Data in Health and Research

“…Moreover, bodies with a commercial interest in selling health-related goods and services may be able to use shared data to pressure people into purchasing them: susceptibility to such pressure is itself a kind of vulnerability to outside influence. This point is touched on in the Domain paper AI-assisted Decision-Making in Healthcare (Lysaght et al 2019). Additionally, certain groups or their members may be vulnerable to unwanted interference by the government or other bodies that is informed by inferences from big data analysis.…”

The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

“…It has to be stated, though, that reported use does not necessarily reflect any improvement of patient outcomes (18). In order to enable a critical evaluation and smooth implementation of new tools in healthcare systems, it is important to ensure that professionals are adequately trained on the benefits and challenges of CDSS before applying them in clinical practice (9).…”

“…The development of computerized clinical support tools can be grouped according to the following main methodologies: (a) information retrieval tools to answer clinical questions and manage medical information ( 4 ); (b) logical models for the assignment of categories for medical standard measurements ( 5 ), characterized alerts and reminder systems ( 6 ); (c) probabilistic and data-driven prediction algorithms to improve patient outcomes ( 7 ); and (d) a modeled combination of formal and heuristic algorithms supporting physicians in their decision on the individual deployment of evidence-based solutions ( 8 , 9 ). Today, these methodologies carry the potential of becoming an innovative resource for digital augmentation of clinical care, once scientifically and clinically validated.…”