Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Supplementary file 1_Harnessing AI for aphasia: a case report on ChatGPT's role in supporting written expression.docx by Avery K. Allen (21449492)

“…While writing aids show promise, artificial intelligence (AI) tools, such as large language models (LLMs), offer new opportunities for individuals with language-based writing challenges.…”

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

“…<p>For all tasks, ophthalmologists’ accuracy is higher when the deep learning model makes the correct decision. For correct classifications, the AI assistance improves grading accuracy. …”

Feature engineering. by Youli Jiang (15249070)

Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. by Liu Liu (512237)

AI driven topic clustering and trend analysis workflow. by Min Ho An (7894658)

Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent. by Liu Liu (512237)

Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power.... by Medhat A. Tawfeek (22522087)

“…The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. …”

Embracing GenAI in Engineering Education: Lessons from the Trenches by Lorena A. Barba (97553)

“…Students quickly developed patterns of using AI as a shortcut rather than a learning companion, leading to decreased attendance and an "illusion of competence." …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

Loss of MAP4K4 expression causes a decrease in the number of neutrophils. by Guihua Wang (286390)

Architecture of Multi-Layer U-Net Model. by Muhammad Zubair (728141)

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GM Mask image count after preprocessing. by Muhammad Zubair (728141)

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Gray matter segmented views of the brain. by Muhammad Zubair (728141)

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