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Found 21 papers, 9 papers with code
Medical Hallucinations in Foundation Models and Their Impact on Healthcare
Our contributions include (1) a taxonomy for understanding and addressing medical hallucinations, (2) benchmarking models using medical hallucination dataset and physician-annotated LLM responses to real medical cases, providing direct insight into the clinical impact of hallucinations, and (3) a multi-national clinician survey on their experiences with medical hallucinations.
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch
Our findings underscore the effectiveness of a customizable, AI-driven JITI system for individuals in need of behavioral intervention in personal undesirable actions.
Human-inspired Perspectives: A Survey on AI Long-term Memory
With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant.
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively.
Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM Agents
Passive tracking methods, such as phone and wearable sensing, have become dominant in monitoring human behaviors in modern ubiquitous computing studies.
Scaling Wearable Foundation Models
Wearable sensors have become ubiquitous thanks to a variety of health tracking features.
Can AI Relate: Testing Large Language Model Response for Mental Health Support
Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS.
G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios
Modern information querying systems are progressively incorporating multimodal inputs like vision and audio.
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
MDAgents achieved the best performance in seven out of ten benchmarks on tasks requiring an understanding of medical knowledge and multi-modal reasoning, showing a significant improvement of up to 4. 2% (p < 0. 05) compared to previous methods' best performances.
Boosting Gesture Recognition with an Automatic Gesture Annotation Framework
We believe that this annotation framework has immense potential to improve the training of downstream gesture recognition models using unlabeled datasets.
Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data
Notably, we observe that our context enhancement can yield up to 23. 8% improvement in performance.
From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self
In the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication.
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
We first conducted a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia.
Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model
(2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system.
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment.
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously.
XAIR: A Framework of Explainable AI in Augmented Reality
The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR.
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images
Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors.
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.
Enabling hand gesture customization on wrist-worn devices
We present a framework for gesture customization requiring minimal examples from users, all without degrading the performance of existing gesture sets.
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging.
