Artificial Intelligence (AI) is transforming healthcare by enhancing diagnostics, personalizing treatment planning, and streamlining patient care. Yet, its adoption is hindered by persistent ethical challenges, including algorithmic bias, lack of transparency, privacy risks, and unclear accountability. Existing international frameworks articulate high-level principles but seldom provide operational guidance for clinical deployment. We bridge this gap by synthesizing trust dimensions for healthcare, with measurable metrics for fairness, explainability, privacy, accountability, and robustness, and proposing the Healthcare AI Trustworthiness Index (HAITI), a composite, context-aware readiness score with explicit normalization, weighting, and uncertainty reporting. We outline a development–deployment–governance blueprint and present two case studies (diagnostic bias mitigation; privacy-preserving federated learning). Together, these contributions translate ethical principles into measurable practices that can foster trust, improve equity, and accelerate responsible AI integration in clinical settings.

Sleep disorders have a major impact on both lifestyle and health. Effective sleep disorder prediction from lifestyle and physiological data can provide essential details for early intervention. This research utilizes three deep time series models and facilitates them with explainability approaches for sleep disorder prediction. Specifically, our approach adopts Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) for time series data analysis, and Temporal Fusion Transformer model (TFT). Meanwhile, the temporal attention mechanism and counterfactual explanation with SHapley Additive exPlanations (SHAP) approach are employed to ensure dependable, accurate, and interpretable predictions. Finally, using a large dataset of sleep health measures, our evaluation demonstrates the effect of our method in predicting sleep disorders.

Drug discovery is a challenging process, requiring the optimization of compounds to become safe and effective. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly, involving multiple rounds of experiments, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost during the process. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep Multimodal Representation Learning framework for accurate Molecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset ( 1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Unified systems for multi-sensor devices, particularly eye-tracking in Virtual Reality (VR), are intricate and often require the listening and streaming of multichannel data. In this project, we propose a visual analysis framework for replicating a participant's viewing involvement by interpreting head movements as rotations and point-of-gaze (POG) as on-screen indicators. Our solution suggests an additional layer of system for near-real-time for processing and analyzing this multi-device data to connect with the data and enable both near-real-time or subsequent offline viewing of the entire VR eye-tracking session. Moreover, our method provides a no-batteries-need solution to create traditional eye-tracking visualization techniques. Finally, we apply three prior education technology analysis metrics: higher density gaze for students, shorter fixation time, and less fixation duration variance for students to determine expertise levels in this system. We systematically establish a ubiquitous, multi-device, eye-tracking solution to incorporate this approach. We evaluate the effectiveness of our system through a user study, using both expertise and non- expertise levels, and selectively surveying to ascertain the quality of the replicated experience and we test the system by running a real-world user study with sixty four different participants. We demonstrate the application's significance and potential to integrate prior analysis metrics using the collected data which this data collection and analysis have been approved by IRB.