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AI in HemOnc and Myelodysplastic Syndromes

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1:05
Using whole genome sequencing to identify novel predictors for SCT in patients with MDS
Wael Saber • 21 Oct 2023
3:01
Challenges in distinguishing between pre-MDS states and MDS, and approaches to improve diagnosis
Klaus Metzeler • 5 May 2023
4:16
Pre-MDS states: identifying patients at risk and using modern tools to improve prognosis
Moshe Mittelman • 5 May 2023
3:36
Accelerating translational research in MDS using AI-generated synthetic data
Matteo Della Porta • 27 Apr 2023
5:55
Validation of non-invasive AI diagnostic tool for MDS
Moshe Mittelman • 5 May 2023
2:33
What can artificial intelligence bring to the diagnosis and management of MDS?
Matteo Della Porta • 27 Apr 2023
3:25
Identifying & monitoring patients with high-risk clonal hematopoiesis
Klaus Metzeler • 5 May 2023
2:44
Implementing machine learning to improve MDS prognostication
Adrián Mosquera Orgueira • 9 Dec 2022
3:39
Applications of AI in hemonc: improving diagnosis & predicting outcomes and response to therapy
Aziz Nazha • 28 Sep 2022
1:21
The use of radiomics and artificial intelligence for sickle cell disease
Raffaella Colombatti • 11 Jun 2022
12:00
MDS: the direction the field is headed
Moshe Mittelman • 13 Sep 2021
2:45
A personalized prediction model to risk stratify patients with MDS
Aziz Nazha
5:47
Using computational algorithms to predict the response of MDS patients to hypomethylating agents
Aziz Nazha • 3 Dec 2016
The content of VJHemOnc is intended for healthcare professionals