112 citations · 215 across the 18 of their papers we have counts for
18 papers
FRAME: Separating sampling variation from performance disparities in medical image analysis
Mahshad Lotfinia, Daniel Truhn, Andreas Maier +1
Fairness audits of medical imaging models commonly report the largest performance difference between demographic subgroups and treat any positive value as evidence of bias. Yet a p…
Medical foundation models converge less under label supervision
Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia +4
Diagnostic classifiers and imaging biomarkers are fitted on the embeddings of medical foundation models. These models are replaced as new versions appear. This practice assumes tha…
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams +4
Vision-language models that answer questions about chest radiographs are evaluated by their accuracy on labels derived from radiology reports. High benchmark accuracy is often inte…
Selective Linkage Precision for Released Medical Vision-Language Models
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung +1
Hospitals may release de-identified chest radiographs while restricting access to their reports. An attacker who already has reports from the same population and their metadata can…
Safety and accuracy diverge across deployment conditions in clinical large language models
Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa +9
Clinical large language models (LLMs) are commonly scaled by increasing model size, context length, retrieval complexity, or inference-time compute, on the assumption that higher a…
Automated grounding verification of AI statements about chest radiographs
Soroosh Tayebi Arasteh, Mehdi Joodaki, Mahshad Lotfinia +4
A grounding verifier checks a statement about a patient's image against the radiology report and the cited evidence. The state-of-the-art methods for testing these verifiers remove…
Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering
Mina Farajiamiri, Jeta Sopa, Saba Afza +9
Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical decision support. Thes…
The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis
Soroosh Tayebi Arasteh, Mina Farajiamiri, Mahshad Lotfinia +7
Differential privacy limits how much each training image can influence a medical imaging model but lowers its diagnostic accuracy. Pretrained weights reduce this loss. It is unknow…
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…
Perceptual implications of automatic anonymization in pathological speech
Soroosh Tayebi Arasteh, Saba Afza, Tri-Thien Nguyen +11
Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain undercharacterized. We present a huma…
Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
Mahshad Lotfinia, Arash Tayebiarasteh, Samaneh Samiei +2
Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets. Federated learning (FL) offers a decentralized and priva…
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…
RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem +7
Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval-augmented generation (RAG) mitigates this by integrating…
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…
How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies
Soroosh Tayebi Arasteh, Mehrpad Monajem, Vincent Christlein +5
Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep le…
Machine Learning-Based Generalized Model for Finite Element Analysis of Roll Deflection During the Austenitic Stainless Steel 316L Strip Rolling
Mahshad Lotfinia, Soroosh Tayebi Arasteh
During the strip rolling process, a considerable amount of the forces of the material pressure cause elastic deformation on the work-roll, i.e., the deflection process. The uncontr…