Project description:Currently, Medical errors are a serious problem when examining patients. Creating information systems that use the capabilities of evidence-based medicine and artificial intelligence methods will allow the doctor to make an informed and proven decision. In this article, the authors offer a description of an information system that solves the problem of supporting medical decision making based on evidence-based medicine. This is achieved by using artificial intelligence methods. This work was supported by a grant from the Ministry of Education and Science of the Russian Federation, a unique project identifier RFMEFI60819X0278.
Project description:ImportanceSurgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.ObservationsSurgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.Conclusions and relevanceIntegration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
Project description:Historically, there has been a great deal of confusion in the literature regarding cross-cultural differences in attitudes towards artificial agents and preferences for their physical appearance. Previous studies have almost exclusively assessed attitudes using self-report measures (i.e., questionnaires). In the present study, we sought to expand our knowledge on the influence of cultural background on explicit and implicit attitudes towards robots and avatars. Using the Negative Attitudes Towards Robots Scale and the Implicit Association Test in a Japanese and Dutch sample, we investigated the effect of culture and robots' body types on explicit and implicit attitudes across two experiments (total n = 669). Partly overlapping with our hypothesis, we found that Japanese individuals had a more positive explicit attitude towards robots compared to Dutch individuals, but no evidence of such a difference was found at the implicit level. As predicted, the implicit preference towards humans was moderate in both cultural groups, but in contrast to what we expected, neither culture nor robot embodiment influenced this preference. These results suggest that only at the explicit but not implicit level, cultural differences appear in attitudes towards robots.Supplementary informationThe online version contains supplementary material available at 10.1007/s12369-022-00917-7.
Project description:The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization criteria (CPC) guidelines. Using machine learning techniques, we have created a tool that can assist clinicians in sorting through the substantial number of referrals they receive each year, leading to more efficient use of clinical specialists' time and improved access to healthcare for patients. Our research included analyzing 17,378 ENT referrals from two hospitals in Queensland between 2019 and 2022. Our results show a level of agreement between referral categories and generated predictions of 53.8%.
Project description:Knowledge without awareness, or implicit knowledge, influences a variety of behaviors. It is unknown however, whether implicit knowledge of statistical structure informs visual perceptual decisions or whether explicit knowledge of statistical probabilities is required. Here, we measured visual decision-making performance using a novel task in which humans reported the orientation of two differently colored translational Glass patterns; each color associated with different orientation probabilities. The task design allowed us to assess participants' ability to learn and use a general orientation prior as well as a color specific feature prior. Classifying decision-makers based on a questionnaire revealed that both implicit and explicit learners implemented a general orientation bias by adjusting the starting point of evidence accumulation in the drift diffusion model framework. Explicit learners additionally adjusted the drift rate offset. When subjects implemented a stimulus specific bias, they did so by adjusting primarily the drift rate offset. We conclude that humans can learn priors implicitly for perceptual decision-making and depending on awareness implement the priors using different mechanisms.
Project description:Background/purposeIn the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis.Materials and methodsThe literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000-February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included.ResultsAI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs).ConclusionThe results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics.
Project description:Humans are strongly affected by social exclusion, a multifaceted and complex phenomenon of social life. However, individuals tend to respond differently depending on a multitude of individual and contextual factors. Firstly, with a view to increasing the ecological validity and experimental control of an exclusion manipulation in the laboratory setting, we made use of immersive virtual environment technology (IVET; an Oculus Rift Virtual Reality headset) to create a new exclusion paradigm. Secondly, given that a recent meta-analytic report on reflexive responses (i.e., affect and physiology) to manipulations of exclusion in the laboratory setting cites inconsistencies across findings (Blackhart et al., 2009), we focused on the form of exclusion manipulated to illustrate how this factor may help to explain the divergences in responses. We thus investigated how explicit and implicit forms of social exclusion may have a differential impact on self-reported affect, as well as on electrodermal and cardiovascular activity. Results from this laboratory study conducted with a varied sample of the local population made salient the affordances of IVET as a tool in exclusion research. They also helped to reconcile the conflicting findings in the literature relating to differences in the level of negative affect generated and shed light on physiological arousal in the wake of being excluded in different ways.
Project description:Older adults’ usage of information and communication technology (ICT) is challenged or facilitated by perception of usefulness, technology design, gender, social class, and other unspoken and political elements. However, studies on the use of ICT by older adults have traditionally focused on explicit interactions (e.g., usability). The article then analyzes how symbolic, institutional, and material elements enable or hinder older adults from using ICT. Our ethnographic methodology includes several techniques with Spanish older adults: 15 semi-structured interviews, participant observation in nine ICT classes, online participant observation on WhatsApp and Jitsi for 3 months, and nine phone interviews due to COVID-19. The qualitative data were analyzed through Situational Analysis. We find that the elements hindering or facilitating ICT practice are implicit-symbolic (children’s surveillance, paternalism, fear, optimism, low self-esteem, and contradictory speech-act), explicit-material (affordances, physical limitations, and motivations), and structural-political (management, the pandemic, teaching, and media skepticism). Furthermore, unprivileged identities hampered the ICT practices: female gender, blue-collar jobs, illiteracy, and elementary education. However, being motivated to use ICT prevailed over having unprivileged identities. The study concludes that society and researchers should perceive older adults as operative with technologies and examine beyond explicit elements. We urge exploration of how older adults’ social identities and how situatedness affects ICT practice. Concerning explicit elements, Spanish authorities should improve and adapt ICT facilities at public senior centers and older adults’ homes, and ICT courses should foster tablet and smartphone training over computers.