Initial validation of the general attitudes towards Artificial Intelligence Scale.
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ABSTRACT:
SUBMITTER: Schepman A
PROVIDER: S-EPMC7231759 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Artificial intelligence (AI) has the potential to improve diagnostic accuracy. Yet people are often reluctant to trust automated systems, and some patient populations may be particularly distrusting. We sought to determine how diverse patient populations feel about the use of AI diagnostic tools, and whether framing and informing the choice affects uptake. To construct and pretest our materials, we conducted structured interviews with a diverse set of actual patients. We then conducted a pre-registered (osf.io/9y26x), randomized, blinded survey experiment in factorial design. A survey firm provided n = 2675 responses, oversampling minoritized populations. Clinical vignettes were randomly manipulated in eight variables with two levels each: disease severity (leukemia versus sleep apnea), whether AI is proven more accurate than human specialists, whether the AI clinic is personalized to the patient through listening and/or tailoring, whether the AI clinic avoids racial and/or financial biases, whether the Primary Care Physician (PCP) promises to explain and incorporate the advice, and whether the PCP nudges the patient towards AI as the established, recommended, and easy choice. Our main outcome measure was selection of AI clinic or human physician specialist clinic (binary, "AI uptake"). We found that with weighting representative to the U.S. population, respondents were almost evenly split (52.9% chose human doctor and 47.1% chose AI clinic). In unweighted experimental contrasts of respondents who met pre-registered criteria for engagement, a PCP's explanation that AI has proven superior accuracy increased uptake (OR = 1.48, CI 1.24-1.77, p < .001), as did a PCP's nudge towards AI as the established choice (OR = 1.25, CI: 1.05-1.50, p = .013), as did reassurance that the AI clinic had trained counselors to listen to the patient's unique perspectives (OR = 1.27, CI: 1.07-1.52, p = .008). Disease severity (leukemia versus sleep apnea) and other manipulations did not affect AI uptake significantly. Compared to White respondents, Black respondents selected AI less often (OR = .73, CI: .55-.96, p = .023) and Native Americans selected it more often (OR: 1.37, CI: 1.01-1.87, p = .041). Older respondents were less likely to choose AI (OR: .99, CI: .987-.999, p = .03), as were those who identified as politically conservative (OR: .65, CI: .52-.81, p < .001) or viewed religion as important (OR: .64, CI: .52-.77, p < .001). For each unit increase in education, the odds are 1.10 greater for selecting an AI provider (OR: 1.10, CI: 1.03-1.18, p = .004). While many patients appear resistant to the use of AI, accuracy information, nudges and a listening patient experience may help increase acceptance. To ensure that the benefits of AI are secured in clinical practice, future research on best methods of physician incorporation and patient decision making is required.
| S-EPMC10198520 | biostudies-literature
Project description:The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".
| S-EPMC9163040 | biostudies-literature
Project description:In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.
| S-EPMC9613950 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has become a powerful tool and is attracting more attention in the field of medicine. There are a number of AI studies focusing on skin diseases, and there are many AI products that have been applied in dermatology. However, the attitudes of dermatologists, specifically those from China, towards AI, is not clear as few, if any studies have focused on this issue.MethodsA web-based questionnaire was designed by experts from the Chinese Skin Image Database (CSID) and published on the UMER Doctor platform (an online learning platform for dermatologists developed by the Shanghai Wheat Color Intelligent Technology Company, China). A total of 1,228 Chinese dermatologists were recruited and provided answers to the questionnaire online. The differences of dermatologists' attitudes towards AI among the different groups (stratified by age, gender, hospital level, education degree, professional title, and hospital ownership) were compared by using the Mann-Whitney U test and the Kruskal-Wallis H test. The correlations between stratified factors and dermatologists' attitudes towards AI were calculated by using the Spearman's rank correlation test. SPSS (version 22.0) was utilized for all analyses. A two-sided P value <0.05 was considered statistically significant in all analyses.ResultsA total of 1,228 Chinese dermatologists from 30 provinces, autonomous regions, municipalities, and other regions (including Hong Kong, Macau, and Taiwan) participated in this survey. The dermatologists who participated acquired AI-related information mainly through the Internet, meetings or forums, and 70.51% of participated dermatologists acquired AI-related information by two or more approaches. In total, 99.51% of participated dermatologists pay attention (general, passive-active, and active attention) to information pertaining to AI. Stratified analyses revealed statistically significant differences in their attention levels (unconcerned, general, passive-active, and active attention) to AI-related information by gender, hospital level, education degree, and professional title (P values ≤1.79E-02). In total, 95.36% of the participated dermatologists thought the role of AI to be in "assisting the daily diagnosis and treatment activities for dermatologists". Stratified analyses about the thought of AI roles (unconcerned, useless, assist, and replace) showed that there was no statistically significant difference except for the hospital level (P value =4.09E-03). The correlations between stratified factors with attention levels and the opinions of AI roles showed extremely weak correlations. Furthermore, 64.17% of participated dermatologists thought secondary hospitals in China are in most need of the application AI, and 91.78% of participated dermatologists thought the priority implementation of AI should be in skin tumors.ConclusionsThe majority of Chinese dermatologists are interested in AI information and acquired information about AI through a variety of approaches. Nearly all dermatologists are attentive to information on AI and think the role of AI is in "assisting the daily diagnosis and treatment activities for dermatologists". Future AI implementation should be primarily focused on skin tumors and utilized in in secondary hospitals.
| S-EPMC7327314 | biostudies-literature
Project description:In two studies we aimed at developing the Attitude towards Italian Mafias Scale (AIMS). In study 1 (N = 292) we used an Exploratory Factor Analysis to reduce the number of the items and explore their latent constructs. In study 2 (N = 393) we performed a Confirmatory Factor Analysis on the resulting 18-item questionnaire, whose latent structure was best identified by a general factor Mafia Attitude and three specific factors related to Behaviors, Cognitions and Emotions-Cognitions towards mafias. Moreover, we showed that the AIMS has (i) discriminant validity compared to a measure of attitudes towards crime, (ii) predictive validity of donation behavior to an association against mafias, (iii) internal consistency, and (iv) invariance for people of the five deep-rooted mafia regions of Southern Italy and those from the rest of Italy. Finally, we observed a difference between the participants from the five deep-rooted mafia regions (i.e., Apulia, Basilicata, Calabria, Campania, Sicily) and the rest of Italy, with the former having surprisingly more negative attitudes towards mafias compared to the latter. The AIMS might help to reliably survey people's sentiment towards Italian mafias and promote targeted and effective law-related education interventions.
| S-EPMC10607589 | biostudies-literature
Project description:The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals' beliefs about AI's influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale's structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users' general attitudes toward AI.
| S-EPMC10406504 | biostudies-literature
Project description:The idea of artificially created social robots has a long tradition. Today, attitudes towards robots play a central role in the field of healthcare. Our research aimed to develop a scale to measure attitudes towards robots. The survey consisted of nine questions on attitudes towards robots, sociodemographic questions, the SWOP-K9, measuring self-efficacy, optimism, and pessimism, and the BFI-10, measuring personality dimensions. Structural relations between the items were detected using principal components analysis (PCA) with Varimax rotation. Correlations and Analysis of Variance were used for external validation. In total, 214 participants (56.1% female, mean age: 30.8 ± 14.4 years) completed the survey. The PCA found two main components, "Robot as a helper and assistant" (RoHeA) and "Robot as an equal partner" (RoEqP), with four items each explaining 53.2% and 17.5% of the variance with a Cronbach's α of 0.915 and 0.768. In the personality traits, "Conscientiousness" correlated weakly with both subscales and "Extraversion" correlated with RoHeA, while none the subscales of the SWOP-K9 significantly correlated with RoEqP or RoHeA. Male participants scored significantly higher than female participants. Our survey yielded a stable and convergent two-factor instrument that exhibited convincing validity and complements other findings in the field. The ASRS can easily be used to describe attitudes towards social robots in human society. Further research, however, should be carried out to investigate the discriminant and convergent validity of the ASRS.
| S-EPMC10855967 | biostudies-literature
Project description:IntroductionThe integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions.MethodsAn online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores.ResultsModerate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps.ConclusionsThis study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.
| S-EPMC10722664 | biostudies-literature
Project description:OBJECTIVES:To explore the attitudes of United Kingdom (UK) medical students regarding artificial intelligence (AI), their understanding, and career intention towards radiology. We also examine the state of education relating to AI amongst this cohort. METHODS:UK medical students were invited to complete an anonymous electronic survey consisting of Likert and dichotomous questions. RESULTS:Four hundred eighty-four responses were received from 19 UK medical schools. Eighty-eight percent of students believed that AI will play an important role in healthcare, and 49% reported they were less likely to consider a career in radiology due to AI. Eighty-nine percent of students believed that teaching in AI would be beneficial for their careers, and 78% agreed that students should receive training in AI as part of their medical degree. Only 45 students received any teaching on AI; none of the students received such teaching as part of their compulsory curriculum. Statistically, students that did receive teaching in AI were more likely to consider radiology (p = 0.01) and rated more positively to the questions relating to the perceived competence in the post-graduation use of AI (p = 0.01-0.04); despite this, a large proportion of students in the taught group reported a lack of confidence and understanding required for the critical use of healthcare AI tools. CONCLUSIONS:UK medical students understand the importance of AI and are keen to engage. Medical school training on AI should be expanded and improved. Realistic use cases and limitations of AI must be presented to students so they will not feel discouraged from pursuing radiology.
| S-EPMC7002761 | biostudies-literature
Project description:IntroductionArtificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI.MethodsWe followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis.ResultsBased on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship.ConclusionOur findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.
| S-EPMC9833517 | biostudies-literature