Beyond the Script: Testing LLMs for Authentic Patient Communication Styles in Healthcare

Effective patient communication is pivotal in healthcare, yet traditional medical training often lacks exposure to diverse, challenging interpersonal dynamics. To bridge this gap, this study proposes the use of Large Language Models (LLMs) to simulate authentic patient communication styles, specifically the "accuser" and "rationalizer" personas derived from the Satir model, while also ensuring multilingual applicability to accommodate diverse cultural contexts and enhance accessibility for medical professionals. Leveraging advanced prompt engineering, including behavioral prompts, author's notes, and stubbornness mechanisms, we developed virtual patients (VPs) that embody nuanced emotional and conversational traits. Medical professionals evaluated these VPs, rating their authenticity (accuser: $3.8 \pm 1.0$; rationalizer: $3.7 \pm 0.8$ on a 5-point Likert scale (from one to five)) and correctly identifying their styles. Emotion analysis revealed distinct profiles: the accuser exhibited pain, anger, and distress, while the rationalizer displayed contemplation and calmness, aligning with predefined, detailed patient description including medical history. Sentiment scores (on a scale from zero to nine) further validated these differences in the communication styles, with the accuser adopting negative ($3.1 \pm 0.6$) and the rationalizer more neutral ($4.0 \pm 0.4$) tone. These results underscore LLMs' capability to replicate complex communication styles, offering transformative potential for medical education. This approach equips trainees to navigate challenging clinical scenarios by providing realistic, adaptable patient interactions, enhancing empathy and diagnostic acumen. Our findings advocate for AI-driven tools as scalable, cost-effective solutions to cultivate nuanced communication skills, setting a foundation for future innovations in healthcare training.

From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant

In online education, innovative tools are crucial for enhancing learning outcomes. SAM (Study with AI Mentor) is an advanced platform that integrates educational videos with a context-aware chat interface powered by large language models. SAM encourages students to ask questions and explore unclear concepts in real-time, offering personalized, context-specific assistance, including explanations of formulas, slides, and images. In a crowdsourced user study involving 140 participants, SAM was evaluated through pre- and post-knowledge tests, comparing a group using SAM with a control group. The results demonstrated that SAM users achieved greater knowledge gains, with a 96.8% answer accuracy. Participants also provided positive feedback on SAM's usability and effectiveness. SAM's proactive approach to learning not only enhances learning outcomes but also empowers students to take full ownership of their educational experience, representing a promising future direction for online learning tools.

TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients

Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggregates the models trained locally by clients without necessitating access to local data. Despite its potential, the implementation of federated learning continues to encounter several challenges, predominantly the slow convergence that is largely due to data heterogeneity. The slow convergence becomes particularly problematic in cross-device federated learning scenarios where clients may be strongly limited by computing power and storage space, and hence counteracting methods that induce additional computation or memory cost on the client side such as auxiliary objective terms and larger training iterations can be impractical. In this paper, we propose a novel federated aggregation strategy, TurboSVM-FL, that poses no additional computation burden on the client side and can significantly accelerate convergence for federated classification task, especially when clients are "lazy" and train their models solely for few epochs for next global aggregation. TurboSVM-FL extensively utilizes support vector machine to conduct selective aggregation and max-margin spread-out regularization on class embeddings. We evaluate TurboSVM-FL on multiple datasets including FEMNIST, CelebA, and Shakespeare using user-independent validation with non-iid data distribution. Our results show that TurboSVM-FL can significantly outperform existing popular algorithms on convergence rate and reduce communication rounds while delivering better test metrics including accuracy, F1 score, and MCC.