Browsing by Author "Uddin, Muhammad Join"

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Healthcare workers' knowledge and attitudes regarding artificial intelligence adoption in healthcare: A cross-sectional study
(2024-12-15) Khan Rony, Moustaq Karim; Akter, Khadiza; Nesa, Latifun; Islam, Md Tawhidul; Johra, Fateha Tuj; Akter, Fazila; Uddin, Muhammad Join; Begum, Jeni; Noor, Md. Abdun; Ahmad, Sumon; Tanha, Sabren Mukta; Khatun, Most. Tahmina; Das Bala, Shuvashish
A cross-sectional research design was employed, incorporating a dual-method approach to select participants using randomness and convenience sampling techniques. Validity was ensured through a literature review, content validity, and reliability assessment (Cronbach's alpha = 0.85), and exploratory factor analysis identified robust underlying factors. Data analysis involved descriptive and inferential statistics, including Fisher's exact tests, multivariate logistic regression, and Pearson correlation analysis, conducted using STATA software, providing a comprehensive understanding of healthcare workers' AI adoption in healthcare. This study revealed that age was a significant factor, with individuals aged 18–25 and 26–35 having higher odds of good knowledge and positive attitudes (AOR 1.56, 95 % CI 1.12–2.43; AOR 1.42, 95 % CI 0.98–2.34). Physicians (AOR 1.08, 95 % CI 0.78–1.89), hospital workers (AOR 1.29, 95 % CI 0.92–2.09), and full-time employees (AOR 1.45, 95 % CI 1.12–2.34) exhibited higher odds. Attending AI conferences (AOR 1.27, 95 % CI 0.92–2.23) and learning through research articles/journals (AOR 1.31, 95 % CI 0.98–2.09) were positively associated with good knowledge and positive attitudes. This research also emphasized the strong correlations between knowledge and positive attitudes (r = 0.89, P < 0.001), as well as negative attitudes with poor knowledge (r = 0.65, P < 0.001). The study highlights the critical need for targeted educational interventions to bridge the knowledge gaps among healthcare professionals regarding AI adoption. The findings reveal that younger healthcare workers, those in full-time employment, and individuals with exposure to AI through conferences or research are more likely to possess good knowledge and hold positive attitudes towards AI integration. These results suggest that policies and training programs must be tailored to address specific demographic differences, ensuring that all groups are equipped to engage with AI technologies. Moreover, the study emphasizes the importance of continuous professional development, which could foster a workforce capable of harnessing AI's potential to improve patient outcomes and healthcare efficiency.
Nurses' Perspectives on Privacy and Ethical Concerns Regarding Artificial Intelligence Adoption in Healthcare
(Elsevier, 2024-08-22) Rony, Moustaq Karim Khan; Numan, Sharker Md.; Akter, Khadiza; Tushar, Hasanuzzaman; Debnath, Mitun; Johra, Fateha tuj; Akter, Fazila; Mondal, Sujit; Dasi, Mousumi; Uddin, Muhammad Join; Begum, Jeni; Parvin, Mst. Rina
Background: With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. Objectives: This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. Methods: We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. Results: Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. Conclusions: The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.