10 citations · 20 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
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.…
Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images
Mohammad Amin Khalafi, Seyed Amir Ahmad Safavi-Naini, Ameneh Salehi +13
Introduction: This study provides a comprehensive performance assessment of vision-language models (VLMs) against established convolutional neural networks (CNNs) and classic machi…
Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models
Nariman Naderi, Seyed Amir Ahmad Safavi-Naini, Thomas Savage +4
This study evaluated self-reported response certainty across several large language models (GPT, Claude, Llama, Phi, Mistral, Gemini, Gemma, and Qwen) using 300 gastroenterology bo…
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models
Seyed Amir Ahmad Safavi-Naini, Shuhaib Ali, Omer Shahab +15
Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used…
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Arian Salahi-Niri +39
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimension…