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2,212 papers
Empirical studies of LLM-enabled AI chatbot applications and evaluations for health purposes across four stakeholder groups: the public or patients, health professionals, health researchers and students, and health institutions are mapped.
Structured prompting improves detection of interpretation and section-consistency errors across seven models and consistently enhances LLM-based error detection, closing the gap between open-source and commercial models and offering a pathway for privacy-preserving, on-premises QA.
Large language models (LLMs) are increasingly extending beyond plain text to jointly reason over images and video. Among such multimodal models, video LLMs that take video as input require the host CPU to decode and preprocess each upcoming batch of video, making it busy throughout training. We show that this property...
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Recent advances in large language models (LLMs), such as GPT-4, have created new opportunities for intelligent computer-assisted language learning, particularly through personalized feedback for multilingual English learners. Building on research in automated writing evaluation (AWE), this study developed an equity...
Abstract Prognosis prediction is important for precision treatment in lung cancer. Chest computed tomography (CT) and deep learning are feasible approaches for learning imaging-based prognostic representations. However, prognostic labels are difficult to obtain because survival endpoints require long-term follow-up; th...
Background/Objectives: Artificial intelligence (AI) is entering interventional pulmonology (IP) faster than its potential risks have been systematically catalogued. We explored whether general-purpose large language models (LLMs), now widely consulted informally by patients and clinicians, could provide a rapid and str...
The dominant narrative casts large language models (LLMs) as a low-cost realization of Bloom’s two-sigma tutor: patient, always available, personalizing at scale. Self-paced online learning, the fastest-growing segment of higher and continuing education and the backbone of the data-science workforce pipeline, is where...
Chatbots based on large language models (LLMs) are commonly used by teenagers for daily communication and introspection. All the previous studies merely compare personality outputs created by a certain AI with no numeric assessment of differences from the self-reported personality scores of students. Questionnaire data...
Reinforcement learning from human feedback (RLHF) and constitutional artificial intelligence (CAI) are two ways to shape model behavior. Among them, CAI is built around avoiding different categories of harm, including racism, sexism, toxic, dangerous and illegal content. This article integrates the existing empirical r...
Cardiovascular disease remains a major contributor to mortality and long-term morbidity worldwide. For this reason, risk prediction, early detection, and decision support have become central tasks in digital health research. Cardiovascular prediction has developed from statistical scores to machine learning and deep le...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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