Societal issues concerning the application of artificial intelligence in medicine

Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain.Peer ReviewedPostprint (author's final draft

Implementation of wavelet encoding spectroscopic imaging technique on a 3 Tesla whole body MR scanner: in vitro results

Proton magnetic resonance spectroscopic imaging (MRSI) provides spatial information about tissue metabolite concentrations used in differentiating diseased from normal tissue. Obtaining metabolic maps with high spatial resolution requires long acquisition time where the patient has to lie still inside the magnet bore (scanner) especially if classical Chemical Shift Imaging (CSI) is used. To reduce acquisition time and obtain a more accurate metabolite distribution with low voxel contamination in MRSI, we have recently proposed and successfully implemented a full Wavelet Encoding-Spectroscopic Imaging (WE-SI) technique on a 1.5 Tesla whole body MR clinical scanner. In this paper we describe the implementation of the WE-SI technique at higher magnetic field strength (B0) on a clinical 3 Tesla Siemens scanner equipped with parallel imaging tools for better sensitivity. This increases the signal to noise ratio (SNR) and allows combination of the proposed technique with the so-called parallel imaging approach for further acquisition time reduction.Peer reviewed: YesNRC publication: Ye

Performance Measurement for Brain-Computer or Brain-Machine Interfaces: A Tutorial

Objective. Brain–computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach. A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main results. Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories. Significance. Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field