Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons.

BACKGROUND: Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use. METHODS: PubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis. RESULTS: Artificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process. CONCLUSIONS: The prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human-machine interaction, have been neglected in the debate on AI for clinical ethics so far. TRIAL REGISTRATION: This review is registered at Open Science Framework ( https://osf.io/wvcs9 ).

Multidimensional gene search with Genehopper.

The high abundance of genetic information enables researchers to gain new insights from the comparison of human genes according to their similarities. However, existing tools that allow the exploration of such gene-to-gene relationships, apply each similarity independently. To make use of multidimensional scoring, we developed a new search engine named Genehopper. It can handle two query types: (i) the typical use case starts with a term-to-gene search, i.e. an optimized full-text search for an anchor gene of interest. The web-interface can handle one or more terms including gene symbols and identifiers of Ensembl, UniProt, EntrezGene and RefSeq. (ii) When the anchor gene is defined, the user can explore its neighborhood by a gene-to-gene search as the weighted sum of nine normalized gene similarities based on sequence homology, protein domains, mRNA expression profiles, Gene Ontology Annotation, gene symbols and other features. Each weight can be adjusted by the user, allowing flexible customization of the gene search. All implemented similarities have a low pairwise correlation (max r(2) = 0.4) implying a low linear dependency, i.e. any change in a single weight has an effect on the ranking. Thus, we treated them as separate dimensions in the search space. Genehopper is freely available at http://genehopper.ifis.cs.tu-bs.de.

A discovery system for narrative query graphs: entity-interaction-aware document retrieval.

Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an initial keyword-based query followed by many refinements to retrieve a sufficiently complete, yet manageable set of documents to satisfy one's information need. Since keyword-based search limits researchers to formulating their information needs as a set of unconnected keywords, retrieval systems try to guess each user's intent. In contrast, distilling short narratives of the searchers' information needs into simple, yet precise entity-interaction graph patterns provides all information needed for a precise search. As an additional benefit, such graph patterns may also feature variable nodes to flexibly allow for different substitutions of entities taking a specified role. An evaluation over the PubMed document collection quantifies the gains in precision for our novel entity-interaction-aware search. Moreover, we perform expert interviews and a questionnaire to verify the usefulness of our system in practice. This paper extends our previous work by giving a comprehensive overview about the discovery system to realize narrative query graph retrieval.