Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A Survey on Artificial Intelligence in Precision Medicine and Healthcare Analysis for Neonatal Surgery
2
Zitationen
2
Autoren
2025
Jahr
Abstract
Artificial Intelligence (AI) has emerged as a transformative force in precision medicine, healthcare analysis, and neonatal surgery, enabling personalized treatment, early disease detection, and optimized clinical decision-making. This survey explores the evolving role of AI in healthcare, focusing on its applications, challenges, and future prospects. AI-driven approaches, including machine learning (ML) and deep learning (DL), have demonstrated remarkable accuracy in medical imaging, genomics, drug discovery, neonatal diagnostics, and patient risk assessment. These technologies enhance diagnostic precision, facilitate predictive analytics, and support real-time monitoring of chronic diseases and neonatal conditions. Precision medicine, which tailors treatments based on an individual’s genetic, environmental, and lifestyle factors, benefits significantly from AI-powered analytics. The integration of AI with electronic health records (EHRs), wearable devices, and biomedical data accelerates early disease identification and personalized therapeutic strategies, including those crucial for neonatal care and surgery. AI models trained on vast healthcare datasets can predict disease progression, recommend targeted therapies, and improve patient outcomes. Furthermore, natural language processing (NLP) enhances clinical documentation, reducing administrative burdens and improving efficiency in healthcare systems. Despite its potential, AI in precision medicine and neonatal surgery faces challenges, including data privacy concerns, model interpretability, and regulatory compliance. Ethical considerations, such as bias in AI models and equitable access to AI-driven healthcare, must be addressed to ensure responsible implementation. Additionally, integrating AI with traditional clinical workflows requires collaboration between healthcare professionals, data scientists, and policymakers. This survey provides a comprehensive analysis of AI applications in precision medicine, healthcare analysis, and neonatal surgery, highlighting key advancements, challenges, and future research directions. As AI continues to evolve, its role in revolutionizing healthcare will expand, paving the way for more efficient, accurate, and patient-centric medical practices. The findings of this survey aim to guide researchers, clinicians, and policymakers in leveraging AI for the next generation of precision healthcare, particularly in neonatal surgical interventions.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.380 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.243 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.671 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 · 5.496 Zit.