Perceptions of Artificial Intelligence in Endoscopy among Italian Gastroenterologists: a National Survey

Today, when digital transformation affects the field of social work, the integration of artificial intelligence technologies into professional practices gains importance. This study examines social work students' views on artificial intelligence with a phenomenological approach. The aim of the study is to determine students' perceptions of artificial intelligence, its potential role in professional practices and their concerns about ethics. The study included 14 third and fourth year social work students living in Bandırma. A semi-structured interview form was used as a data collection tool, and in-depth interviews lasted between 30-45 minutes. The analyses were carried out by thematic analysis method using MAXQDA software. In the findings, 8 sub-themes and 27 codes were identified under 3 main themes: 'Perceptions of Artificial Intelligence', 'Intersection of Social Work and Artificial Intelligence' and 'Artificial Intelligence Education and Access'. While students emphasise the benefits of artificial intelligence such as making life easier (f=10) and fast access (f=8), they have concerns about lack of empathy (f=8), privacy and data security (f=8). In addition, the integration of artificial intelligence into the curriculum (f=9) emerged as an important need. The results show that artificial intelligence in social work education should be positioned in line with professional values.

Artificial intelligence (AI) is being increasingly explored in different domains of gastroenterology, particularly in endoscopic image analysis, cancer screening, and prognostication models. It is widely touted to become an integral part of routine endoscopies, considering the bulk of data handled by endoscopists and the complex nature of critical analyses performed. However, the application of AI in endoscopy in resource-constrained settings remains fraught with problems. We conducted an extensive literature review using the PubMed database on articles covering the application of AI in endoscopy and the difficulties encountered in resource-constrained settings. We have tried to summarize in the present review the potential problems that may hinder the application of AI in such settings. Hopefully, this review will enable endoscopists and health policymakers to ponder over these issues before trying to extrapolate the advancements of AI in technically advanced settings to those having constraints at multiple levels.How to cite this article: Anirvan P, Meher D, Singh SP. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check. Euroasian J Hepato-Gastroenterol 2020;10(2): 92–97.

The study aimed to assess teachers' perceptions of artificial intelligence (AI), their understanding of its potential advantages and disadvantages, and the specific training needs necessary for successful AI integration. The study aimed to assess teachers' perceptions of artificial intelligence (AI), their understanding of its potential advantages and disadvantages, and the specific training needs necessary for successful AI integration. A mixed-method research design was used in collecting and analyzing quantitative and qualitative data. Descriptive statistics was utilized to delineate teachers' perspectives on AI in education. Open-ended questions were employed to gather insights into the challenges teachers encounter when using AI in the classroom. The findings revealed that teachers' experiences with AI present opportunities and challenges in integrating technology into the classroom. While teachers expressed optimism about AI's potential to enhance education, citing benefits such as personalized learning, educator support, and administrative efficiency, they also voiced concerns about increased workload, excessive student reliance on technology, algorithmic bias within AI systems, and data privacy. A balanced approach to AI integration is recommended not only that complements traditional teaching methods and addresses privacy and algorithmic bias. Schools are encouraged to offer comprehensive professional development programs for teachers to introduce AI technologies, address the ethical and practical challenges associated with their use in education, ensure equitable access to AI tools, and improve technological infrastructure. Keywords: Artificial Intelligence, Capacity building ,Educators, Education, private schools,

Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile. The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education. This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview. The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others (P < 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic. Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified.

Introduction Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the development of endoscopic diagnosis, there is a shortage of specialists who can perform high-precision endoscopy. We will examine whether AI with excellent image recognition ability can overcome this problem. Areas covered Since 2016, papers on artificial intelligence using convolutional neural network (CNN in other word Deep Learning) have been published. CNN is generally capable of more accurate detection and classification than conventional machine learning. This is a review of papers using CNN in the gastrointestinal endoscopy area, along with the reasons why AI is required in clinical practice. We divided this review into four parts: stomach, esophagus, large intestine, and capsule endoscope (small intestine). Expert opinion Potential applications for the AI include colorectal polyp detection and differentiation, gastric and esophageal cancer detection, and lesion detection in capsule endoscopy. The accuracy of endoscopic diagnosis will increase if the AI and endoscopist perform the endoscopy together.

Background: Artificial Intelligence (AI), recognized as the fourth industrial revolution, is revolutionizing medical practice through enhanced diagnostics, treatment planning, and healthcare delivery. Its expanded role post-COVID-19 underscores the urgency of AI literacy among future physicians. This study aimed to explore medical students’ perceptions of AI in medical education. Method: A cross-sectional study was conducted from June to July 2024 at CMH and AIMC medical colleges in Pakistan. Using a non-probability convenience sampling technique, an online questionnaire was distributed among MBBS students from both public and private institutions. A minimum of 10% of students from each academic year were invited, yielding a final sample of 139 respondents (response rate: 92%). Associations between categorical variables were analyzed using the chi-square test. Results: Among participants, 57.2% were female and 42.8% male. Most students (96.4%) agreed that computers aid learning, and 93.5% believed AI will be essential in future medical practice. Additionally, 77% felt AI would improve medical education, while 87.7% expressed willingness to receive AI-based instruction. A majority (89.1%) supported the inclusion of AI training in the curriculum, and 83.5% believed AI knowledge would benefit their careers. Female students were significantly more likely to support curriculum integration (p = 0.03) and AI instruction (p = 0.04), though no significant gender differences were observed in perceptions of AI’s future role (p = 0.12). Conclusion: Medical students exhibited highly positive attitudes toward AI. While they do not view AI as replacing physicians, they recognize its transformative role. Integrating AI education into MBBS curricula is therefore essential.

This research was carried out with the aim of investigating the effect of employees’ perception of artificial intelligence on the willingness to accept and implement artificial intelligence in knowledge-based companies. The statistical population of this research is the employees of knowledge-based companies across the Iran, because their number is unrestricted, 384 people were determined as the sample size and data was collected through an electronic questionnaire. The questions of the questionnaire were adapted from the article of Ahn and Chen 2022. In this research, Amos24 software was used to test hypotheses and model structural equations. The results showed that employees’ perceptions of AI (including 1: understanding the benefits of AI; 2: understanding the concerns of AI; 3: understanding the vision of AI; and 4: experience of using and ease of access to AI) have an impact on the willingness to adopt and implement AI in knowledge-based companies.

This study investigated the knowledge and perception of artificial intelligence among library and information science professionals in Zambia by surveying 245 randomly selected participants. The results show that the library and information science professionals had a favourable and encouraging outlook with regard to artificial intelligence but were also concerned about artificial intelligence replacing librarians’ roles and the barriers to adopting artificial intelligence technologies in Zambian libraries. The article suggests that libraries should consider the study’s findings before implementing artificial intelligence, particularly concerning technology and facilities, librarians’ proficiency in artificial intelligence, and leadership positions in artificial intelligence initiatives. The research can be used as a resource by library boards and associations to develop policies for implementing artificial intelligence in academic libraries, and fills a research vacuum in developing nations like Zambia regarding the knowledge of university and college libraries, and their willingness to use artificial intelligence.

This paper investigated GP perception of artificial intelligence in relation to symptom checking, and specifically whether GPs view artificial intelligence as an opportunity or a threat. The authors advocate for a sustained collaboration between GPs and artificial intelligence as the way forward for patient and societal benefit. A collaborative approach requires broad-level adoption of artificial intelligence-enabled applications to complement rather than replace a GP's own expertise. Drawing on extant literature, this study investigated how measures of self-efficacy, being an individual's ability to believe in their own ability to organise and implement courses of action, influence the perception of artificial intelligence as an opportunity rather than as a threat. Prior work suggested that higher measures of self-efficacy would correlate with the view that artificial intelligence is an opportunity. In this work, 110 GPs from the UK were invited to be surveyed via a structured questionnaire about perceived self-efficacy and view of artificial intelligence. Of these, 26 GPs agreed to participate, giving a response rate of 24%. The results gave preliminary evidence that higher levels of perceived self-efficacy were associated with greater perceptions of artificial intelligence either as an opportunity or as a threat. This finding offers a new perspective for policy makers, leaders and academics, who are looking for predictors of artificial intelligence engagement. This work may form the basis for further research on the potential causal relationship between self-efficacy and AI adoption, which could ultimately help facilitate artificial intelligence adoption.

ObjectiveThis survey aims to understand frontline healthcare professionals’ perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions.Materials and MethodsWe conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants’ familiarity and experience with AI on the survey results.ResultsFrom 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = −0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups.DiscussionThe findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity.ConclusionThe study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals’ perspective. These insights can guide strategies for successful AI implementation in healthcare.

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

Educators have called for training in artificial intelligence (AI) in medical education given its certain impact on the future of healthcare. However, there is no consensus regarding how to introduce AI into medical education and little is known about how AI is viewed among medical trainees. In an effort to inform the development of medical education curricula on AI, this study explores perceptions of resident physicians regarding AI in healthcare and its possible impact on their future practice. The authors conducted focus groups with resident physicians across multiple specialties in 2018-2019. Residents were invited to voluntarily participate during pre-existing conference times. Interview transcripts were coded iteratively, and coded data was clustered into categories and themes to capture resident perceptions on AI. Fifty-six residents from emergency medicine, internal medicine, pathology, pediatrics, and radiology participated in six separate focus groups. Conversations generated the following five overarching themes: healthcare is transforming, AI has a role at the clinical and systems level, concern for lack of agency in the development and implementation of AI, AI presents potential harms and uncertainties, and enduring roles of the physician: humanism, judgment, and responsibility. Residents described humanistic roles that should not be replaced by technology and voiced concerns that physicians lack agency to influence how AI will be used in healthcare. Medical education should explore humanistic and ethical challenges related to AI, provide a foundational understanding of AI technology, and offer opportunities for participation in the development of AI technology when possible.

The rapid integration of artificial intelligence (AI) technologies into academic and professional practice has profoundly shaped the communication and public relations domain. However, there is limited empirical understanding of how individuals actually perceive and use these new technologies. Therefore, this article investigates Communication and Public Relations (PR) students’ perceptions of artificial intelligence (AI), specifically aiming to understand how literacy, psychological factors, and trust influence their AI word-of-mouth (WOM). We proposed a theoretical model, which we tested using a quantitative approach with SmartPLS analysis on data gathered from 402 online questionnaires administered to students across three major Romanian universities. The key findings indicate that Internet use fosters AI literacy, which subsequently enhances both AI self-efficacy and the perceived ease of AI use. Crucially, higher AI literacy leads to greater trust, promoting informed choices. While AI self-efficacy encourages appropriate reliance on the technology and positively impacts behavioral intention toward AI, higher trust surprisingly leads to lower privacy concerns. Conversely, heightened privacy concerns increase algorithm aversion, which in turn negatively impacts both behavioral intention and WOM communication about AI. We also confirm that a positive behavioral intention is a strong predictor of increased WOM communication. These findings have significant implications for academia, policymakers, and PR practitioners by highlighting the necessity of boosting AI literacy to mitigate aversion and foster responsible AI adoption among future communication professionals.

Introduction With the current exponential expansion of robotics, implants, and imaging technologies, diagnostic processes within the healthcare industryare becoming popular platforms for artificial intelligence (AI) use.Thus, an understanding of physicians' attitudes toward AI and the extent to which medical educators are ready to work with AI is necessary.This research aimed tostudy doctors' perceptions of AI in healthcare. Methods A web-based questionnaireorganized into four sections, namely, demographics, concepts of AI, education in AI, and implementation challenges related to AI, was designed systematically based on a literature search and circulated among medical doctors from various fields. Results Study participants exhibited a lower score toward familiarity with AI. Only 52.12% (74/142) of physicians completed the survey. The greatest challenge associated with the use of AI in therapeutic settings was found to be the degree of autonomy,with a score of 3.56. Among the participants, 67.61% felt that the lack of human supervision was the most important limiting factor in the implementation of AI in clinical practice. However, the participants demonstrated a strong interest in understanding the concepts of AI in the near future. Conclusion This study revealed a low degree of familiarity with AI,highlighting the need for medical schools and hospitals to establish specialized education and training programs for physicians to improve patient outcomes.

This chapter delves into the perceptions of artificial intelligence (AI) experts on the societal implications, governance, and ethical responsibilities associated with AI. Drawing on qualitative research, including interviews with six AI experts, the study investigates three key questions: experts' perceptions of AI's societal impacts, their visions for an AI-governed society, and their views on their responsibilities in addressing AI's consequences. The findings suggest that AI experts emphasize the importance of balancing innovation with education and regulation to ensure AI's responsible development and application, leaving out important questions to analyze further, such as their role in AI political governance, and the interest of wide public participation in the process of AI creation.