🚨 #PhDPosition Available 🚨
Exciting opportunity in Explainable AI in healthcare @Qmul
🌍 Location: London
🧑🏫 Supervisor: Dr Evangelia Kyrimi
📅 Deadline: 22 November 2024
🔗 Apply here: https://t.co/Z3iBzvhYed
#PhD#Research#AcademicTwitter#PhDJobs
We have commenced a Delphi study to inform our understanding of what it means for AI to be explainable in healthcare. We welcome expressions of interest and participation from experts from a diverse research background who wish to contribute. https://t.co/cqWjvULBsH
What do you think is the role that causality should play in explainable AI?
To discuss recent research on causal XAI, come and join us this October at the Causal XAI Workshop. https://t.co/fI28bGIUOA
@WilliamMarsh_QM@david_lagnado
New review paper by our .@RIM_QMUL research group (lead author .@LinaKyrimi) sheds light on why there has been relatively little adoption of #Bayesian network solutions in healthcare: https://t.co/CgOnT1zDYH
Our new paper led by @LinaKyrimi published today in Journal of Biomedical Informatics provides practical guidelines for building effective #BayesianNetwork models for medical decision support. Accepted paper is here: https://t.co/7tL9uDAp9v
The problem with doing COVID research is that by the time stuff gets published it's already out of date. Anyway here's a piece by our group on policy aspects of contact tracing (we'd predicted problems with the NHS app) https://t.co/DlONvLLVio