Ethics of Artificial Intelligence Demarcations

In this paper we present a set of key demarcations, particularly important when discussing ethical and societal issues of current AI research and applications. Properly distinguishing issues and concerns related to Artificial General Intelligence and weak AI, between symbolic and connectionist AI, AI methods, data and applications are prerequisites for an informed debate. Such demarcations would not only facilitate much-needed discussions on ethics on current AI technologies and research. In addition sufficiently establishing such demarcations would also enhance knowledge-sharing and support rigor in interdisciplinary research between technical and social sciences.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019), Trondheim, Norwa

Developing a comprehensive information security framework for mHealth: a detailed analysis

It has been clearly shown that mHealth solutions, which is the use of mobile devices and other wireless technology to provide healthcare services, deliver more patient-focused healthcare, and improve the overall efficiency of healthcare systems. In addition, these solutions can potentially reduce the cost of providing healthcare in the context of the increasing demands of the aging populations in advanced economies. These solutions can also play an important part in intelligent environments, facilitating real-time data collection and input to enable various functionalities. However, there are several challenges regarding the development of mHealth solutions: the most important of these being privacy and data security. Furthermore, the use of cloud computing is becoming an option for the healthcare sector to store healthcare data; but storing data in the cloud raises serious concerns. This paper investigates how data are managed both on mHealth devices as well as in the cloud. Firstly, a detailed analysis of the entire mHealth domain is undertaken to determine domain-specific features and a taxonomy for mHealth, from which a set of security requirements are identified in order to develop a new information security framework. It then examines individual information security frameworks for mHealth devices and the cloud, noting similarities and differences. Furthermore, key mechanisms to implement the new framework are discussed and the new framework is then presented. Finally, the paper presents how the new framework could be implemented in order to develop an Advanced Digital Medical Platform

Comparing counselling alone versus counselling supplemented with guided use of a well-being app for university students experiencing anxiety or depression (CASELOAD): protocol for a feasibility trial.

BACKGROUND: University counselling services face a unique challenge to offer short-term therapeutic support to students presenting with complex mental health needs and in a setting which suits the academic timetable. The recent availability of mobile phone applications (apps) offers an opportunity to supplement face-to-face therapy and has the potential to reach a wider audience, maintain engagement between therapy sessions, and enhance therapeutic outcomes. The present study, entitled Counselling plus Apps for Students Experiencing Levels of Anxiety or Depression (CASELOAD), aims to explore the feasibility of supplementing counselling with guided use of a well-being app. METHODS/DESIGN: Forty help-seeking university students (aged 18 years and over) with symptoms of moderate anxiety or depression will be recruited from a University Counselling Service (UCS) in the United Kingdom (UK). Participants will be recruited via counsellors who provide the initial clinical assessment and who determine treatment allocation to one of two treatments on the basis of client-treatment fit. The two conditions comprise (1) counselling alone (treatment as usual/TAU) or (2) counselling supplemented with guided use of a well-being app (enhanced intervention). Trained counsellors will deliver up to six counselling sessions in each treatment arm across a 6-month period, and the session frequency will be decided by client-counsellor discussion. Assessments will occur at baseline, every counselling session, post-intervention (3 months after consent) and follow-up (6 months after consent). Assessments will include clinical measures of anxiety, depression, psychological functioning, specific mental health concerns (e.g. academic distress and substance misuse), resilience and therapeutic alliance. The usage, acceptability, feasibility and potential implications of combining counselling with guided use of the well-being app will be assessed through audio recordings of counselling sessions, telephone interviews with participants, focus groups with counsellors and counsellor notes. DISCUSSION: This study will inform the design of a randomised pilot trial and a definitive trial which aim to improve therapy engagement, reduce dropout and enhance clinical outcomes of student counselling. TRIAL REGISTRATION: ISRCTN55102899

Effectiveness of internet-based interventions for children, youth, and young adults with anxiety and/or depression: a systematic review and meta-analysis

BACKGROUND: The majority of internet-based anxiety and depression intervention studies have targeted adults. An increasing number of studies of children, youth, and young adults have been conducted, but the evidence on effectiveness has not been synthesized. The objective of this research is to systematically review the most recent findings in this area and calculate overall (pooled) effect estimates of internet-based anxiety and/or depression interventions. METHODS: We searched five literature databases (PubMed, EMBASE, CINAHL, PsychInfo, and Google Scholar) for studies published between January 1990 and December 2012. We included studies evaluating the effectiveness of internet-based interventions for children, youth, and young adults (age <25 years) with anxiety and/or depression and their parents. Two reviewers independently assessed the risk of bias regarding selection bias, allocation bias, confounding bias, blinding, data collection, and withdrawals/dropouts. We included studies rated as high or moderate quality according to the risk of bias assessment. We conducted meta-analyses using the random effects model. We calculated standardized mean difference and its 95% confidence interval (95% CI) for anxiety and depression symptom severity scores by comparing internet-based intervention vs. waitlist control and internet-based intervention vs. face-to-face intervention. We also calculated pooled remission rate ratio and 95% CI. RESULTS: We included seven studies involving 569 participants aged between 7 and 25 years. Meta-analysis suggested that, compared to waitlist control, internet-based interventions were able to reduce anxiety symptom severity (standardized mean difference and 95% CI = −0.52 [−0.90, −0.14]) and increase remission rate (pooled remission rate ratio and 95% CI =3.63 [1.59, 8.27]). The effect in reducing depression symptom severity was not statistically significant (standardized mean difference and 95% CI = −0.16 [−0.44, 0.12]). We found no statistical difference in anxiety or depression symptoms between internet-based intervention and face-to-face intervention (or usual care). CONCLUSIONS: The present analysis indicated that internet-based interventions were effective in reducing anxiety symptoms and increasing remission rate, but not effective in reducing depression symptom severity. Due to the small number of higher quality studies, more attention to this area of research is encouraged. TRIAL REGISTRATION: PROSPERO registration: CRD4201200210

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions

Background Mobile health (mHealth) is often reputed to be cost-effective or cost-saving. Despite optimism, the strength of the evidence supporting this assertion has been limited. In this systematic review the body of evidence related to economic evaluations of mHealth interventions is assessed and summarized. Methods Seven electronic bibliographic databases, grey literature, and relevant references were searched. Eligibility criteria included original articles, comparison of costs and consequences of interventions (one categorized as a primary mHealth intervention or mHealth intervention as a component of other interventions), health and economic outcomes and published in English. Full economic evaluations were appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist and The PRISMA guidelines were followed. Results Searches identified 5902 results, of which 318 were examined at full text, and 39 were included in this review. The 39 studies spanned 19 countries, most of which were conducted in upper and upper-middle income countries (34, 87.2%). Primary mHealth interventions (35, 89.7%), behavior change communication type interventions (e.g., improve attendance rates, medication adherence) (27, 69.2%), and short messaging system (SMS) as the mHealth function (e.g., used to send reminders, information, provide support, conduct surveys or collect data) (22, 56.4%) were most frequent; the most frequent disease or condition focuses were outpatient clinic attendance, cardiovascular disease, and diabetes. The average percent of CHEERS checklist items reported was 79.6% (range 47.62–100, STD 14.18) and the top quartile reported 91.3–100%. In 29 studies (74.3%), researchers reported that the mHealth intervention was cost-effective, economically beneficial, or cost saving at base case. Conclusions Findings highlight a growing body of economic evidence for mHealth interventions. Although all studies included a comparison of intervention effectiveness of a health-related outcome and reported economic data, many did not report all recommended economic outcome items and were lacking in comprehensive analysis. The identified economic evaluations varied by disease or condition focus, economic outcome measurements, perspectives, and were distributed unevenly geographically, limiting formal meta-analysis. Further research is needed in low and low-middle income countries and to understand the impact of different mHealth types. Following established economic reporting guidelines will improve this body of research