Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Review of the 2024 Spring Conference of the Korean Society of Medical Informatics – Omnibus Omnia
0
Zitationen
6
Autoren
2024
Jahr
Abstract
The 2024 Spring Academic Conference of the Korean Society of Medical Informatics (KOSMI) marked a significant milestone in the field of medical informatics with its theme, "Omnibus Omnia."Continuing KOSMI's 36-year tradition of annual meetings, the conference convened healthcare professionals, researchers, industry experts, and policymakers (Table 1).The satisfaction survey revealed that among the 304 respondents, 212 (69.7%) were in their 20s and 30s.This group included 25 (8.2%) undergraduate students and 83 (27.3%) graduate students, emphasizing the strong presence of young and emerging researchers and students.The attendees' professional backgrounds were diverse: 63 (20.7%) were researchers, 46 (15.1%) professors, 47 (15.5%) industry professionals, and 40 (13.2%) from various other fields.This varied participation highlights the conference's broad appeal and its interdisciplinary nature.The opening session, led by Organizing Committee Chairman Kim Dae-Jin, focused on the theme "Omnibus Omnia." He expressed his enthusiasm for exploring the impact of Fourth Industrial Revolution technologies, big data generative research, and artificial intelligence on healthcare.He also highlighted the conference as a valuable opportunity to share knowledge about these advancements.Society President Han Ho-Seong predicted that artificial intelligence (AI) in healthcare would establish a new paradigm, distinct from traditional medicine.He expressed his commitment to enhancing the medical environment through health information technology, which encompasses various technological trends aimed at improving the quality of life and health for many.Hwang Jong-Sung, President of the National Information Society Agency, emphasized the significance of data and AI as tools that support human endeavors.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.418 Zit.