45 citations · 109 across the 5 of their papers we have counts for
5 papers
Multi-step retrieval and reasoning improves radiology question answering with large language models
Sebastian Wind, Jeta Sopa, Daniel Truhn +9
Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-aug…
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
Marziyeh Mohammadi, Mohsen Vejdanihemmat, Mahshad Lotfinia +4
Differential privacy (DP) is a key technique for protecting sensitive patient data in medical deep learning (DL). As clinical models grow more data-dependent, balancing privacy wit…
From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine
Lukas Buess, Matthias Keicher, Nassir Navab +2
Generative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical wo…
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Paula Andrea Perez-Toro +6
Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensiti…
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
Soroosh Tayebi Arasteh, Christiane Kuhl, Marwin-Jonathan Saehn +3
Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multi…