Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine Learning and Emerging Technologies in Cancer Care
0
Zitationen
2
Autoren
2026
Jahr
Abstract
Cancer is the second-leading global cause of death (>10M/year), posing complex economic and social burdens. Current treatments—conventional (chemo, radio) and advanced (targeted, immuno)—are limited by high cost, toxicity, lack of precision, and unpredictable patient response . An urgent paradigm shift requires leveraging Artificial Intelligence (AI) and Machine Learning (ML). AI/ML drives breakthroughs in the entire oncological care pathway: enabling precise diagnosis, robust prognosis, and individualized prediction of therapeutic response via multi-omics analysis. The future of oncology must be defined by AI-powered precision to solve the problem of variable efficacy and achieve universal access to effective care.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.402 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.270 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.702 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.507 Zit.