activity
20242026
most citedVision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models

10 citations · 20 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

A Unified Framework for Comprehensive Cardiac CT Segmentation and Phenotyping: Human-in-the-Loop Data Annotation, Vision Foundation Model Development, Multicenter Evaluation and Clinical Validation

Pooya Mohammadi Kazaj, Leo Fridolin Weber, Wen Xie +17

Comprehensive quantification of cardiac structures from computed tomography (CT) remains limited not by data availability but by the scalability of measurements, which makes routin…

cs.AI2026

Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework

Seyed Amir Ahmad Safavi-Naini, Elahe Meftah, Josh Mohess +11

The competency of any intelligent agent is bounded by its formal account of the world in which it operates. Clinical AI lacks such an account. Existing frameworks address evaluatio…

eess.IV2026

3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset

Mehran Advand, Zahra Dehghanian, Navid Faraji +3

Existing medical imaging datasets for abdominal CT often lack three-dimensional annotations, multi-organ coverage, or precise lesion-to-organ associations, hindering robust represe…

eess.IV2025

State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability

Saeide Danaei, Zahra Dehghanian, Elahe Meftah +4

This systematic review critically evaluates publicly available abdominal CT datasets and their suitability for artificial intelligence (AI) applications in clinical settings. We ex…

cs.CY2025★ 1 cited

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

Nariman Naderi, Zahra Atf, Peter R Lewis +3

This paper investigates how prompt engineering techniques impact both accuracy and confidence elicitation in Large Language Models (LLMs) applied to medical contexts. Using a strat…

cs.AI2025★ 1 cited

The challenge of uncertainty quantification of large language models in medicine

Zahra Atf, Seyed Amir Ahmad Safavi-Naini, Peter R. Lewis +4

This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications.…