Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
JMIR human factors
Emotion-Adaptive Large Language Model-Driven Clinical Decision Support: User Evaluation of the Empathic Clinical Decision Support System Framework for Trust and Explainability
BACKGROUND: The increasing prevalence of cannabis use has motivated researchers to develop computational behavioral models that predict usage patterns and related health impacts in naturalistic environments. However, the opaque nature of many artificial intelligence (AI) systems limits users' ability to interpret outputs and undermines trust. Existing explainable artificial intelligence techniques often remain overly technical and do not account for the confusion or frustration clinicians may experience when interpreting complex model explanations. OBJECTIVE: We propose and evaluate an empathic clinical decision support system (empathic-CDSS) that integrates large language models (LLMs) with real-time emotion recognition and explainability modules. The goal is to provide transparent, adaptive, and emotionally attuned explanations that enhance interpretability, user confidence, and trust in AI-assisted clinical decision-making. METHODS: Our empathic-CDSS integrates explainable artificial intelligence, causal inference, and affective computing within an interactive LLM-driven framework. Users' affective states were inferred using the circumplex model of affect, which characterizes emotions along 2 dimensions: valence (the degree of pleasure or displeasure) and arousal (the level of physiological activation or energy). Based on the captured emotional signals, the system dynamically adjusts the tone, structure, style, and complexity of its explanations generated by a fine-tuned LLM, enabling personalized and plain-language explanations. A total of 33 participants with diverse medical and technical backgrounds engaged with the system through guided evaluation tasks and postsession assessments to evaluate 6 dimensions of user-centered questions: usability, personalization and relevance of insights, clarity and comprehensibility, system benefits, satisfaction, and trust and reliability. RESULTS: Our empathic-CDSS effectively generated personalized and transparent explanations that revealed the causal reasoning behind model predictions while enhancing users' emotional engagement and trust in the system's decision logic. Continuous, affect-based feedback enabled the system to dynamically adapt explanation delivery to individual user needs. Participants reported significantly improved usability, clarity, satisfaction, and trust compared with a baseline clinical decision support system without emotion adaptation. Improvements were also observed across additional evaluation dimensions, including personalization and perceived system benefits, supporting the feasibility and added value of integrating empathy-aware communication into AI-driven clinical decision support. CONCLUSIONS: This study introduces a transparent, trustworthy, and emotionally adaptive framework for AI-assisted prediction and clinical decision support. By uniting causal reasoning, affective sensing, and LLM-based natural-language explanations, the empathic-CDSS offers a novel direction for developing emotionally intelligent and user-centered AI systems, with potential applications in behavioral monitoring and personalized interventions, including cannabis use and related health domains.
23 May 2026
Read appraisal →Science advances
Wearable thumb sleeves enabled by self-supervised learning with few stretchable sensors and few-shot data for switchable finger tasks
Human fingers exhibiting remarkable dexterity are ideal for natural human-machine interaction. Traditional methods require at least one device per finger and extensive labeled data, often limiting models to a single user and task. Here, we propose a wearable thumb sleeve integrated with self-supervised learning, which exhibits user independence and data efficiency, enabling recognition of various finger-related tasks. The thumb sleeve is equipped with only two stretchable sensors at the thumb joints and learns latent features from unlabeled random thumb movement data. By using fine-tuning with five-shot labeled data, it can rapidly adapt to new users and tasks, including eight directional commands and 10 knuckle key inputs. It allows free switching between tasks without the need to reconstruct or retrain the model. The proposed approach demonstrates strong potential for real-world applications, serving as a substitute for a mouse and keyboard to enable tasks such as online shopping.
25 Apr 2026
Read appraisal →PloS one
Vision Transformer attention alignment with human visual perception in aesthetic object evaluation
Visual attention mechanisms play a crucial role in human perception and aesthetic evaluation. Recent advances in Vision Transformers (ViTs) have demonstrated remarkable capabilities in computer vision tasks, yet their alignment with human visual attention patterns remains underexplored, particularly in aesthetic contexts. This study investigates the correlation between human visual attention and ViT attention mechanisms when evaluating handcrafted objects. We conducted an eye-tracking experiment with 30 participants (9 female, 21 male, mean age 24.6 years) who viewed 20 artisanal objects comprising basketry bags and ginger jars. Using a Pupil Labs eye-tracker, we recorded gaze patterns and generated heatmaps representing human visual attention. Simultaneously, we analyzed the same objects using a pre-trained ViT model with DINO (Self-DIstillation with NO Labels), extracting attention maps from each of the 12 attention heads. We compared human and ViT attention distributions using four complementary metrics-Kullback-Leibler divergence, Structural Similarity Index (SSIM), Pearson's Correlation Coefficient (CC), and Similarity (SIM)-across varying Gaussian parameters ([Formula: see text]), yielding 1,152,000 distance evaluations. Additionally, we performed Areas of Interest (AOI) analysis to quantify ViT attention concentration within object regions. Statistical analysis revealed optimal correlation at [Formula: see text], with attention head #12 showing the strongest alignment with human visual patterns across all metrics. Significant differences were found between attention heads, with heads #7 and #9 demonstrating the greatest divergence from human attention ([Formula: see text]), Tukey HSD test). AOI analysis confirmed that all ViT heads concentrated attention significantly more within object regions than background areas ([Formula: see text]), with heads #12, #1, and #3 achieving lift values of +30 to +40 percentage points. Results indicate that while ViTs exhibit more global attention patterns compared to human focal attention, certain attention heads can approximate human visual behavior, particularly for specific object features like buckles in basketry items. These findings suggest potential applications of ViT attention mechanisms in product design and aesthetic evaluation, while highlighting fundamental differences in attention strategies between human perception and current AI models.
6 Apr 2026
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