6 papers
Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs
Mahdi Alkaeed, Adnan Qayyum, Nabeel Abo Kashreef +2
Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic variations, such as rephrasing or…
Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review
Ufaq Khan, Umair Nawaz, Adnan Qayyum +5
Recent advancements in machine learning (ML) and deep learning (DL), particularly through the introduction of Foundation Models (FMs), have significantly enhanced surgical scene un…
Uncovering Privacy Vulnerabilities through Analytical Gradient Inversion Attacks
Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino +2
Federated learning has emerged as a prominent privacy-preserving technique for leveraging large-scale distributed datasets by sharing gradients instead of raw data. However, recent…
TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation
Muhammad Bilal, Mahmood Alam, Deepa Bapu +16
Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for systematic retrospective analysis and clinical performance evaluation. Despite its sign…
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription
Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum +6
In response to the success of proprietary Large Language Models (LLMs) such as OpenAI's GPT-4, there is a growing interest in developing open, non-proprietary LLMs and AI foundatio…
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients
Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino +3
In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mec…