Artificial intelligence (AI) provides a unique opportunity to help meet the demands of the future healthcare system. However, hospitals may not be well equipped to handle safe and effective development and/or procurement of AI systems. Furthermore, upcoming regulations such as the EU AI Act may enforce the need to establish new management systems, quality assurance and control mechanisms, novel to healthcare organizations. This paper discusses challenges in AI implementation, particularly potential gaps in current management systems (MS), by reviewing the harmonized standard for AI MS, ISO 42001, as part of a gap analysis of a tertiary acute hospital with ongoing AI activities. Examination of the industry agnostic ISO 42001 reveals a technical debt within healthcare, aligning with previous research on digitalization and AI implementation. To successfully implement AI with quality assurance in mind, emphasis should be put on the foundation and structure of the healthcare organizations, including both workforce and data infrastructure.
Purpose of Review: The incidence of sport-related concussion (SRC) has been increasing in different sports and its impact on long-term cognitive function is increasingly recognized. In this study, we review the epidemiology, neuropathophysiology, clinical symptoms, and long-term consequences of SRC with a specific focus on cognition. Recent Findings: Repeated concussions are associated with an increased risk of several neurologic diseases and long-term cognitive deficits. To improve cognitive outcomes in athletes with SRC, standardized guidelines for the assessment and management of SRC are vital. However, current concussion management guidelines lack procedures for rehabilitating acute and long-term cognitive symptoms. Summary: Increased awareness for the management and rehabilitation of cognitive symptoms in SRC is needed in all clinical neurologists treating professional and amateur athletes. We propose cognitive training as a prehabilitation tool to alleviate the severity of cognitive symptoms and as a rehabilitative tool to improve cognitive recovery postinjury.
More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.
Access to health data, important for population health planning, basic and clinical research and health industry utilization, remains problematic. Legislation intended to improve access to personal data across national borders has proven to be a double-edged sword, where complexity and implications from misinterpretations have paradoxically resulted in data becoming more siloed. As a result, the potential for development of health specific AI and clinical decision support tools built on real-world data have yet to be fully realized. In this perspective, we propose federated networks as a solution to enable access to diverse data sets and tackle known and emerging health problems. The perspective draws on experience from the World Economic Forum Breaking Barriers to Health Data project, the Personal Health Train and Vantage6 infrastructures, and industry insights. We first define the concept of federated networks in a healthcare context, present the value they can bring to multiple stakeholders, and discuss their establishment, operation and implementation. Challenges of federated networks in healthcare are highlighted, as well as the resulting need for and value of an independent orchestrator for their safe, sustainable and scalable implementation.
Delivery of Health Care , Privacy , United States
Cognition-oriented treatments - commonly categorized as cognitive training, cognitive rehabilitation and cognitive stimulation - are promising approaches for the prevention of cognitive and functional decline in older adults. We conducted a systematic overview of meta-analyses investigating the efficacy of cognition-oriented treatments on cognitive and non-cognitive outcomes in older adults with or without cognitive impairment. Review quality was assessed by A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR). We identified 51 eligible reviews, 46 of which were included in the quantitative synthesis. The confidence ratings were "moderate" for 9 (20%), "low" for 13 (28%) and "critically low" for 24 (52%) of the 46 reviews. While most reviews provided pooled effect estimates for objective cognition, non-cognitive outcomes of potential relevance were more sparsely reported. The mean effect estimate on cognition was small for cognitive training in healthy older adults (mean Hedges' g = 0.32, range 0.13-0.64, 19 reviews), mild cognitive impairment (mean Hedges' g = 0.40, range 0.32-0.60, five reviews), and dementia (mean Hedges' g = 0.38, range 0.09-1.16, seven reviews), and small for cognitive stimulation in dementia (mean Hedges' g = 0.36, range 0.26-0.44, five reviews). Meta-regression revealed that higher AMSTAR score was associated with larger effect estimates for cognitive outcomes. The available evidence supports the efficacy of cognition-oriented treatments improving cognitive performance in older adults. The extent to which such effects are of clinical value remains unclear, due to the scarcity of high-quality evidence and heterogeneity in reported findings. An important avenue for future trials is to include relevant non-cognitive outcomes in a more consistent way and, for meta-analyses in the field, there is a need for better adherence to methodological standards. PROSPERO registration number: CRD42018084490.
Cognitive Dysfunction/therapy , Aged , Cognition , Humans , Meta-Analysis as Topic , Systematic Reviews as Topic
BACKGROUND: Dementia is the leading cause of disability worldwide, and interventions aimed at reducing the prevalence and burden of the disease are urgently needed. Maintain Your Brain (MYB) is a randomized controlled trial of a multimodal digital health intervention targeting modifiable dementia risk factors to combat cognitive decline and potentially prevent dementia. In addition to behavioral modules targeting mood, nutrition, and physical exercise, a new Brain Training System (BTS) will deliver computerized cognitive training (CCT) throughout the trial to provide systematic, challenging, and personally adaptive cognitive activity. OBJECTIVE: This paper aimed to describe the design and development of BTS. METHODS: BTS has been designed with a central focus on the end user. Raw training content is provided by our partner NeuroNation and delivered in several innovative ways. A baseline cognitive profile directs selection and sequencing of exercises within and between sessions and is updated during the 10-week 30-session module. Online trainers are available to provide supervision at different levels of engagement, including face-to-face share-screen coaching, a key implementation resource that is triaged by a "red flag" system for automatic tracking of user adherence and engagement, or through user-initiated help requests. Individualized and comparative feedback is provided to aid motivation and, for the first time, establish a social support network for the user based on their real-world circle of friends and family. RESULTS: The MYB pilot was performed from November 2017 to March 2018. We are currently analyzing data from this pilot trial (n=100), which will make up a separate research paper. The main trial was launched in June 2018. Process and implementation data from the first training module (September to November 2018) are expected to be reported in 2019 and final trial outcomes are anticipated in 2022. CONCLUSIONS: The BTS implemented in MYB is focused on maximizing adherence and engagement with CCT over the short and long term in the setting of a fully digital trial, which, if successful, could be delivered economically at scale. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000851268; https://www.anzctr.org.au /Trial/Registration/TrialReview.aspx?id=370631&isReview=true.
Sports performance at the highest level requires a wealth of cognitive functions such as attention, decision making, and working memory to be functioning at optimal levels in stressful and demanding environments. Whilst a substantial research base exists focusing on psychological skills for performance (e.g., imagery) or therapeutic techniques for emotion regulation (e.g., cognitive behavioral therapy), there is a scarcity of research examining whether the enhancement of core cognitive abilities leads to improved performance in sport. Cognitive training is a highly researched method of enhancing cognitive skills through repetitive and targeted exercises. In this article, we outline the potential use of cognitive training (CT) in athlete populations with a view to supporting athletic performance. We propose how such an intervention could be used in the future, drawing on evidence from other fields where this technique is more fruitfully researched, and provide recommendations for both researchers and practitioners working in the field.
Objective: To quantitatively aggregate effects of cognitive training (CT) on cognitive and functional outcome measures in patients with traumatic brain injury (TBI) more than 12-months post-injury. Design: We systematically searched six databases for non-randomized and randomized controlled trials of CT in TBI patients at least 12-months post-injury reporting cognitive and/or functional outcomes. Main Measures: Efficacy was measured as standardized mean difference (Hedges' g) of post-training change. We investigated heterogeneity across studies using subgroup analyses and meta-regressions. Results: Fourteen studies encompassing 575 patients were included. The effect of CT on overall cognition was small and statistically significant (g = 0.22, 95%CI 0.05 to 0.38; p = 0.01), with low heterogeneity (I2 = 11.71%) and no evidence of publication bias. A moderate effect size was found for overall functional outcomes (g = 0.32, 95%CI 0.08 to 0.57, p = 0.01) with low heterogeneity (I2 = 14.27%) and possible publication bias. Statistically significant effects were also found only for executive function (g = 0.20, 95%CI 0.02 to 0.39, p = 0.03) and verbal memory (g = 0.32, 95%CI 0.14 to 0.50, p < 0.01). Conclusion: Despite limited studies in this field, this meta-analysis indicates that CT is modestly effective in improving cognitive and functional outcomes in patients with post-acute TBI and should therefore play a more significant role in TBI rehabilitation.
OBJECTIVE: To quantify the effects of cognitive training (CT) on cognitive and behavioral outcome measures in patients with Parkinson disease (PD). METHODS: We systematically searched 5 databases for randomized controlled trials (RCTs) of CT in patients with PD reporting cognitive or behavioral outcomes. Efficacy was measured as standardized mean difference (Hedges g) of post-training change. RESULTS: Seven studies encompassing 272 patients with Hoehn & Yahr Stages 1-3 were included. The overall effect of CT over and above control conditions was small but statistically significant (7 studies: g = 0.23, 95% confidence interval [CI] 0.014-0.44, p = 0.037). True heterogeneity across studies was low (I(2) = 0%) and there was no evidence of publication bias. Larger effect sizes were noted on working memory (4 studies: g = 0.74, CI 0.32-1.17, p = 0.001), processing speed (4 studies: g = 0.31, CI 0.01-0.61, p = 0.04), and executive function (5 studies: g = 0.30, CI 0.01-0.58, p = 0.042), while effects on measures of global cognition (4 studies), memory (5 studies), visuospatial skills (4 studies), and depression (5 studies), as well as attention, quality of life, and instrumental activities of daily living (3 studies each), were not statistically significant. No adverse events were reported. CONCLUSIONS: Though still small, the current body of RCT evidence indicates that CT is safe and modestly effective on cognition in patients with mild to moderate PD. Larger RCTs are necessary to examine the utility of CT for secondary prevention of cognitive decline in this population.
Cognition Disorders/therapy , Cognitive Behavioral Therapy/methods , Parkinson Disease/therapy , Activities of Daily Living/psychology , Aged , Cognition/physiology , Cognition Disorders/diagnosis , Cognition Disorders/psychology , Female , Humans , Male , Middle Aged , Parkinson Disease/diagnosis , Parkinson Disease/psychology , Randomized Controlled Trials as Topic/methods
Computerized cognitive training (CCT) is a safe and inexpensive intervention to enhance cognitive performance in the elderly. However, the neural underpinning of CCT-induced effects and the timecourse by which such neural changes occur are unknown. Here, we report on results from a pilot study of healthy older adults who underwent three 1-h weekly sessions of either multidomain CCT program (n = 7) or an active control intervention (n = 5) over 12 weeks. Multimodal magnetic resonance imaging (MRI) scans and cognitive assessments were performed at baseline and after 9 and 36 h of training. Voxel-based structural analysis revealed a significant Group × Time interaction in the right post-central gyrus indicating increased gray matter density in the CCT group compared to active control at both follow-ups. Across the entire sample, there were significant positive correlations between changes in the post-central gyrus and change in global cognition after 36 h of training. A post-hoc vertex-based analysis found a significant between-group difference in rate of thickness change between baseline and post-training in the left fusiform gyrus, as well as a large cluster in the right parietal lobe covering the supramarginal and post-central gyri. Resting-state functional connectivity between the posterior cingulate and the superior frontal gyrus, and between the right hippocampus and the superior temporal gyrus significantly differed between the two groups after 9 h of training and correlated with cognitive change post-training. No significant interactions were found for any of the spectroscopy and diffusion tensor imaging data. Though preliminary, our results suggest that functional change may precede structural and cognitive change, and that about one-half of the structural change occurs within the first 9 h of training. Future studies are required to determine the role of these brain changes in the mechanisms underlying CCT-induced cognitive effects.
Functional compensation in late life is poorly understood but may be vital to understanding long-term cognitive trajectories. To study this we first established an empirically derived threshold to distinguish hippocampal atrophy in those with Mild Cognitive Impairment (MCI n = 34) from those with proficient cognition (PRO n = 22), using data from a population-based cohort. Next, to identify compensatory networks we compared cortical activity patterns during a graded spatial working memory (SWM) task in only cognitively proficient individuals, either with (PROATR ) or without hippocampal atrophy (PRONIL ). Multivariate Partial Least Squares analyses revealed that these groups engaged spatially distinct SWM-related networks. In those with hippocampal atrophy and under conditions of basic-SWM demand, expression of a posterior compensatory network (PCN) comprised calcarine and posterior parietal cortex strongly correlated with superior SWM performance (r = -0.96). In these individuals, basic level SWM response times were faster and no less accurate than in those with no hippocampal atrophy. Cognitively proficient older individuals with hippocampal atrophy may, therefore, uniquely engage posterior brain areas when performing simple spatial working memory tasks.
Cognitive Dysfunction/pathology , Hippocampus/pathology , Hippocampus/physiopathology , Memory, Short-Term/physiology , Spatial Memory/physiology , Aged , Aged, 80 and over , Atrophy , Brain Mapping , Cohort Studies , Female , Humans , Least-Squares Analysis , Magnetic Resonance Imaging , Male , Multivariate Analysis , Neural Pathways/pathology , Neural Pathways/physiopathology , Neuropsychological Tests , Organ Size , Parietal Lobe/physiopathology , Signal Processing, Computer-Assisted
BACKGROUND: New effective interventions to attenuate age-related cognitive decline are a global priority. Computerized cognitive training (CCT) is believed to be safe and can be inexpensive, but neither its efficacy in enhancing cognitive performance in healthy older adults nor the impact of design factors on such efficacy has been systematically analyzed. Our aim therefore was to quantitatively assess whether CCT programs can enhance cognition in healthy older adults, discriminate responsive from nonresponsive cognitive domains, and identify the most salient design factors. METHODS AND FINDINGS: We systematically searched Medline, Embase, and PsycINFO for relevant studies from the databases' inception to 9 July 2014. Eligible studies were randomized controlled trials investigating the effects of ≥ 4 h of CCT on performance in neuropsychological tests in older adults without dementia or other cognitive impairment. Fifty-two studies encompassing 4,885 participants were eligible. Intervention designs varied considerably, but after removal of one outlier, heterogeneity across studies was small (I(2)â= 29.92%). There was no systematic evidence of publication bias. The overall effect size (Hedges' g, random effects model) for CCT versus control was small and statistically significant, g = 0.22 (95% CI 0.15 to 0.29). Small to moderate effect sizes were found for nonverbal memory, g = 0.24 (95% CI 0.09 to 0.38); verbal memory, g = 0.08 (95% CI 0.01 to 0.15); working memory (WM), g = 0.22 (95% CI 0.09 to 0.35); processing speed, g = 0.31 (95% CI 0.11 to 0.50); and visuospatial skills, g = 0.30 (95% CI 0.07 to 0.54). No significant effects were found for executive functions and attention. Moderator analyses revealed that home-based administration was ineffective compared to group-based training, and that more than three training sessions per week was ineffective versus three or fewer. There was no evidence for the effectiveness of WM training, and only weak evidence for sessions less than 30 min. These results are limited to healthy older adults, and do not address the durability of training effects. CONCLUSIONS: CCT is modestly effective at improving cognitive performance in healthy older adults, but efficacy varies across cognitive domains and is largely determined by design choices. Unsupervised at-home training and training more than three times per week are specifically ineffective. Further research is required to enhance efficacy of the intervention. Please see later in the article for the Editors' Summary.
Cognition Disorders/prevention & control , Cognition , Computers , Dementia/prevention & control , Learning , Memory , Aged , Humans , Middle Aged
