AIMC Topic: Humans

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A gamification training system designed according to a mental model structure: A case study of universal robots.

Scientific reports
As part of the industrial revolution, the collaborative robot (cobot) has become increasingly important in Industry 5.0. However, the most significant barrier for the industry to adopt the cobot is a lack of knowledge and skills. Therefore, e-learnin...

Human visual attention-inspired knowledge distillation underlying interpretable computational pathology.

Scientific reports
Computational pathology leverages advanced deep-learning techniques to analyze medical images with high resolution. However, a trade-off exists between model lightweight, interpretability, and task performance in such real-world scenarios. Knowledge ...

An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars.

Scientific reports
The practical adoption of deep learning in high-stakes forensic applications, such as dental age estimation, is often limited by the 'black box' nature of the models. This study introduces a framework designed to enhance both performance and transpar...

The global epidemiology, risk factors, and mortality prediction of nocardiosis: an easily missed opportunistic infection.

Scientific reports
This study was to comprehensively investigate the epidemiology of nocardiosis worldwide and develop an interpretable machine learning (ML) model to predict mortality in patients with nocardiosis. The PubMed and Web of Science databases were searched ...

AUPA: weakly supervised approach for streamlining breast cancer diagnostic workflow by WSI histological type classification for efficient IHC triage.

Scientific reports
In routine breast cancer diagnostics, pathologists often review each case twice-first to determine the need for immunohistochemical (IHC) stains, and a second time to issue the final diagnosis-creating significant workload and delays. We present an a...

Integrating machine-learning and nanotechnology to quantify pH-modulated oxaliplatin release.

Scientific reports
The purpose of this work was to formulate and characterize pH-sensitive, surfactant-based nanomicelles for the targeted delivery of Oxaliplatin to breast cancer cells. A secondary aim was to utilize machine learning (ML) models to interpolate and dis...

The pitfalls of multiple-choice questions in generative AI and medical education.

Scientific reports
The performance of Large Language Models (LLMs) on multiple-choice question (MCQ) benchmarks is frequently cited as proof of their medical capabilities. We hypothesized that LLM performance on medical MCQs may in part be illusory and driven by factor...

Transformer-based deep learning for adaptive pedagogy under uncertain student preferences.

Scientific reports
As educational environments become increasingly heterogeneous, conventional teaching strategies often fall short in accommodating the diverse and evolving learning behaviors of students, particularly when individual learning preferences are ambiguous...

A Hybrid Cross-Attentive CNN-BiLSTM-Transformer Network for Dysarthria Severity Classification.

Scientific reports
Dysarthria is a neurological speech disorder characterized by articulatory impairment due to muscle weakness. Objective automated detection and severity classification of dysarthria enables timely intervention and tailored clinical management. Here, ...

Reconstructing music perception from brain activity using a prior guided diffusion model.

Scientific reports
Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-su...