Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Unveiling the Benefits of Artificial Intelligence in Individual, Organizational Management, and the Health/Sector System
1
Zitationen
2
Autoren
2025
Jahr
Abstract
Background: Artificial intelligence (AI) is transforming healthcare by improving care, efficiency, and cost benefit, yet limited research compares its benefits across individual, organizational, and system levels to guide effective integration. Aims: This review examines how the adoption of AI can benefit healthcare by improving individual outcomes, enhancing organizational efficiency, and strengthening the healthcare system. Methods: Two reviewers screened studies published up to December 2023 from four databases-a three-dimensional framework identified AI benefits for individuals, organizations, and the broader health sector. Results: A total of 92 articles met the inclusion criteria, showing AI provides meaningful benefits across all dimensions. The individual benefits dimension is precise but often context-specific, showing substantial gains in early detection, diagnosis, and monitoring. AI reduces the workload by 19%-50%, improves reading times by 21%-54%, and enhances cancer detection by 20% and treatment timing. The organizational benefits dimension encompasses AI-optimized workflows and cost reductions, enhancing efficiency in clinical settings and workflow management. The health sector system dimension shows the most substantial evidence, highlighting improved patient outcomes, workflow efficiency, data access, training, and overall system advancement. Despite these advantages, challenges remain regarding data integrity, patient safety, and privacy under strict healthcare regulations. Conclusion: AI offers benefits at all levels, with the most substantial evidence being at the system level, demonstrating clear improvements in patient care and workflow. Individual and organizational gains are promising, though more validation is needed. Future adoption should prioritize cost-benefit analysis and system-wide AI integration while evaluating impacts at all levels.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.719 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.628 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.176 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.880 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.