Journal-Article

Across generations, sizes, and types, large language models poorly report self-confidence in gastroenterology clinical reasoning tasks
This study evaluated confidence calibration across 48 large language models (LLMs) using 300 gastroenterology board exam-style …
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The impact of image size on bloodstain pattern analysis using machine learning
Classification of bloodstain patterns can occur at the scene or through (digital) images submitted for analysis and/or peer review. …
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Unsupervised machine learning for the detection and interpretation of key features in drip patterns
Bloodstain pattern analysis (BPA) is increasingly shifting towards more objective methodologies for pattern classification. This …
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A Survey of Accessible Explainable Artificial Intelligence Research
The increasing integration of artificial intelligence (AI) into everyday life makes it essential to explain AI-based decision-making in …
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Large language models versus classical machine learning performance in COVID-19 mortality prediction using high-dimensional tabular data
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in …
Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Aryan Salahi-Niri, Hossein Toreyhi, Zahra Atf, Amirali Mohsenzadeh-Kermani, Mahshad Sarikhani, Zohreh Tajabadi, Fatemeh Shojaeian, Mohammad Hassan Bagheri, Aydin Feyzi, Mohamadamin Tarighat-Payma, Narges Gazmeh, Fateme Heydari, Hossein Afshar, Amirreza Allahgholipour, Farid Alimardani, Ameneh Salehi, Naghmeh Asadimanesh, Mohammad Amin Khalafi, Hadis Shabanipour, Ali Moradi, Sajjad Hossein Zadeh, Omid Yazdani, Romina Esbati, Moozhan Maleki, Danial Samiei Nasr, Amirali Soheili, Hossein Majlesi, Saba Shahsavan, Alireza Soheilipour, Nooshin Goudarzi, Erfan Taherifard, Hamidreza Hatamabadi, Jamil S. Samaan, Thomas Savage, Ankit Sakhuja, Ali Soroush, Girish Nadkarni, Ilad Alavi Darazam, Mohamad Amin Pourhoseingholi, Seyed Amir Ahmad Safavi-Naini
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Initial validity and reliability testing of the SGBA-5
Background A growing body of research indicates that sex (biological) and gender (sociocultural) influence health through a variety of …
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Exploring Accessible Explainable AI: Promising Avenues
The concept of Accessible Explainable Artificial Intelligence (AXAI) addresses the need for inclusive XAI design, focusing on …
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Towards inclusive explainable artificial intelligence: a thematic analysis and scoping review on tools for persons with disabilities
Objective: Explainable Artificial Intelligence (XAI) offers transparent, trustworthy decision support, yet its implementation in …
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Is Trust Correlated With Explainability in AI? A Meta-Analysis
This study critically examines the commonly held assumption that explicability in artificial intelligence (AI) systems inherently …
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Reflective Artificial Intelligence
As artificial intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today’s AI systems …
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