215 citations · 465 across the 15 of their papers we have counts for
5 papers · 1 filter
Medical Foundation Models are Susceptible to Targeted Misinformation Attacks
Tianyu Han, Sven Nebelung, Firas Khader +9
Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia +4
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical da…
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Teresa Nolte +6
Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data. The use of AI models solely trained on large multi…
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis
Firas Khader, Gustav Mueller-Franzes, Tianci Wang +9
Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn…
Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Soroosh Tayebi Arasteh, Peter Isfort, Marwin Saehn +6
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training…