4 citations · 6 across the 7 of their papers we have counts for
7 papers
Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care
Burcu Sayin, Ngoc Vo Hong, Ipek Baris Schlicht +8
Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as interactive aids in live physic…
Hybrid Decision Making via Conformal VLM-generated Guidance
Debodeep Banerjee, Burcu Sayin, Stefano Teso +1
Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We work in the context of learnin…
Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Markus Reuter, Tobias Lingenberg, Rūta Liepiņa +5
Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically de…
MedSyn: Enhancing Diagnostics with Human-AI Collaboration
Burcu Sayin, Ipek Baris Schlicht, Ngoc Vo Hong +4
Clinical decision-making is inherently complex, often influenced by cognitive biases, incomplete information, and case ambiguity. Large Language Models (LLMs) have shown promise as…
MedGellan: LLM-Generated Medical Guidance to Support Physicians
Debodeep Banerjee, Burcu Sayin, Stefano Teso +1
Medical decision-making is a critical task, where errors can result in serious, potentially life-threatening consequences. While full automation remains challenging, hybrid framewo…
Noiser: Bounded Input Perturbations for Attributing Large Language Models
Mohammad Reza Ghasemi Madani, Aryo Pradipta Gema, Gabriele Sarti +3
Feature attribution (FA) methods are common post-hoc approaches that explain how Large Language Models (LLMs) make predictions. Accordingly, generating faithful attributions that r…
Rethinking and Recomputing the Value of Machine Learning Models
Burcu Sayin, Jie Yang, Xinyue Chen +2
In this paper, we argue that the prevailing approach to training and evaluating machine learning models often fails to consider their real-world application within organizational o…