Efficiency of Health Investment: Education or Intelligence?
Ontology highlight
ABSTRACT:
SUBMITTER: Bijwaard GE
PROVIDER: S-EPMC5064047 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAs the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.ObjectiveWith a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness.MethodsAfter the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career).ResultsOf the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.ConclusionsThis review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
| S-EPMC8713099 | biostudies-literature
Project description:BACKGROUND:The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. OBJECTIVE:The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. METHODS:A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. RESULTS:This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. CONCLUSIONS:To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
| S-EPMC7367541 | biostudies-literature
Project description:The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.
| S-EPMC6870216 | biostudies-literature
Project description:Intelligence and education are predictive of better physical and mental health, socioeconomic position (SEP), and longevity. However, these associations are insufficient to prove that intelligence and/or education cause these outcomes. Intelligence and education are phenotypically and genetically correlated, which makes it difficult to elucidate causal relationships. We used univariate and multivariable Mendelian randomization to estimate the total and direct effects of intelligence and educational attainment on mental and physical health, measures of socioeconomic position, and longevity. Both intelligence and education had beneficial total effects. Higher intelligence had positive direct effects on income and alcohol consumption, and negative direct effects on moderate and vigorous physical activity. Higher educational attainment had positive direct effects on income, alcohol consumption, and vigorous physical activity, and negative direct effects on smoking, BMI and sedentary behaviour. If the Mendelian randomization assumptions hold, these findings suggest that both intelligence and education affect health.
| S-EPMC6748790 | biostudies-literature
Project description:Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.
| S-EPMC6918207 | biostudies-literature
Project description:Intelligence test scores and educational duration are positively correlated. This correlation could be interpreted in two ways: Students with greater propensity for intelligence go on to complete more education, or a longer education increases intelligence. We meta-analyzed three categories of quasiexperimental studies of educational effects on intelligence: those estimating education-intelligence associations after controlling for earlier intelligence, those using compulsory schooling policy changes as instrumental variables, and those using regression-discontinuity designs on school-entry age cutoffs. Across 142 effect sizes from 42 data sets involving over 600,000 participants, we found consistent evidence for beneficial effects of education on cognitive abilities of approximately 1 to 5 IQ points for an additional year of education. Moderator analyses indicated that the effects persisted across the life span and were present on all broad categories of cognitive ability studied. Education appears to be the most consistent, robust, and durable method yet to be identified for raising intelligence.
| S-EPMC6088505 | biostudies-other
Project description:Research shows that mindfulness practice can alter the expression of genes associated with energy metabolism, telomere maintenance, inflammatory and stress response. The aim of this study was to determine if mindfulness awareness practice (MAP) or health education program (HEP) might reverse cognitive impairment and/or prevent further cognitive decline in 60 subjects (aged 60-90). We investigated the gene expression changes in subject with mild cognitive impairment randomized to either group after 9 months. We used Affymetrix GeneChip Human Genome U133 Plus 2.0 array to determine the differential gene expression between Pre- and Post-treatment samples from MAP and HEP group.
2017-12-19 | GSE108215 | GEO
Project description:OBJECTIVE:To systematically investigate causal relationships between educational attainment, cognitive-related phenotypes, and amyotrophic lateral sclerosis (ALS). METHODS:Summary statistics from genome-wide association studies for educational attainment, math ability, highest math class taken, cognitive performance, intelligence, and ALS were used. A two-sample Mendelian randomization (MR) design was applied to explore the potential causal associations between them. RESULTS:Genetically predisposition to longer educational attainment and harder math class taken were associated with significantly lower statistical odds ratio of ALS. For per year increase in education completed there was a 21% lower (95% confidence interval [CI] = 27-14%) in risk for ALS. For per 1-SD increase in the highest math class taken obtained there was a 19% lower (95% CI = 9-28%) in risk for ALS. Genetically predisposition to math ability, cognitive performance, and intelligence did not decrease the risk of ALS. INTERPRETATION:This study provides genetic support for a causal association between higher educational attainment and a lower risk of ALS. The genes related to these phenotypes are involved in almost all aspects of neuron-to-neuron communication. ALS patients are occasionally accompanied by varying degrees of cognitive impairment. People with greater cognitive reserve may be better able to offset damages of degenerative brain changes associated with dementia or other brain diseases, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, or ALS.
| S-EPMC7480912 | biostudies-literature
Project description:BackgroundIt is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature.ObjectiveA scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond.MethodsThis scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories.ResultsA total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice.ConclusionsCurricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings.International registered report identifier irridRR2-10.2196/17490.
| S-EPMC8328269 | biostudies-literature
Project description:Evidence of socioeconomic inequality in COVID-19-related outcomes is emerging, with a higher risk of infection and mortality observed among individuals with lower education attainment. We aimed to evaluate the potential interventions against COVID-19 from the socioeconomic perspective, including improvement in education and intelligence. With a two-sample Mendelian randomization approach using summary statistics from the largest genome-wide association meta-analysis, univariable analysis was adopted to evaluate the total causal effects of genetically determined education attainment and intelligence on COVID-19 outcomes. Multivariable analysis was performed to dissect the potential mechanisms. Genetic predisposition to higher education attainment by 1 SD (4.2 years) was independently associated with reduced risk of COVID-19 severity (OR = 0.508 [95% CI: 0.417-0.617]; p < 0.001). Genetically higher education attainment also lowered the risk of COVID-19 hospitalization (0.685 [0.593-0.791]; p < 0.001), but the association was attenuated after adjustment for beta estimates of intelligence in multivariable analysis. Genetically higher intelligence was associated with reduced risk of COVID-19 hospitalization (0.780 [0.655-0.930]; p = 0.006), with attenuation of association after adjustment for education attainment. Null association was observed for genetically determined education attainment and intelligence with SARS-CoV-2 infection. Education may act independently and jointly with intelligence in improving the COVID-19 outcomes. Improving education may potentially alleviate the COVID-19-related health inequality.
| S-EPMC8584527 | biostudies-literature