Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Data Science in Oncology: A Summary of the Mini-Symposium at the IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology
0
Zitationen
5
Autoren
2023
Jahr
Abstract
The 2023 IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology will host a plenary session discussing the state of data science in Oncology and its challenges moving forward. Cancer is the second most leading cause of death and a very heterogenous disease. Data science and artificial intelligence (AI) have made very promising advancements in Oncology to combat this large heterogeneity and the wide range of available treatments. In this session we will discuss data science in the different pillars of cancer care including Surgical, Radiation, and Interventional Oncology.Clinical Relevance: This is a brief summary of the Data Science in Oncology plenary session.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.102 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.468 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 · 5.429 Zit.