Ilmu Komputer & AI editorial
Open AccessOA2025
Advancing Technology at the Intersection of AI, Machine Learning, and Cybersecurity: Applications in Healthcare, Poultry Science, and Computer Science
This digest summarizes how AI, ML, and cybersecurity are transforming healthcare, poultry science, and computer science through predictive analytics, automation, and secure data management, while highlighting challenges in privacy, ethics, and technical constraints.
Mohammed Ali· Global Trends in Science and Technology· 2025· DOI 10.70445/gtst.1.4.2025.47-62
The core problem
The article explores the transformative role of Artificial Intelligence (AI), Machine Learning (ML), and cybersecurity across three domains: healthcare, poultry science, and computer science. It posits that these technologies enable predictive analytics, automation, and secure data management, fostering interdisciplinary innovation, performance, and resilience. The introduction sets the stage for a cross-domain analysis, emphasizing the potential to revolutionize research and work, while acknowledging challenges related to data privacy, ethics, and technical limitations.
Innovation
The source text does not provide a detailed methodology section. It appears to be a review or perspective article synthesizing applications and implications of AI, ML, and cybersecurity across the mentioned fields. As such, the methodology likely involves a literature review and conceptual analysis, but specific methods are not described in the provided content.
Introduction
The article explores the transformative role of Artificial Intelligence (AI), Machine Learning (ML), and cybersecurity across three domains: healthcare, poultry science, and computer science. It posits that these technologies enable predictive analytics, automation, and secure data management, fostering interdisciplinary innovation, performance, and resilience. The introduction sets the stage for a cross-domain analysis, emphasizing the potential to revolutionize research and work, while acknowledging challenges related to data privacy, ethics, and technical limitations.
Why it matters
The discussion highlights the synergistic potential of AI, ML, and cybersecurity when applied across domains. It suggests that interdisciplinary approaches can lead to breakthroughs in predictive analytics, automation, and secure data management. However, the article also notes that addressing privacy, ethical, and technical challenges is crucial for realizing the full benefits. The overall implication is that these technologies will continue to shape the future of research and work, driving efficiency and resilience in diverse sectors.
Who should read this
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