Xiaoye Michael Wang

Xiaoye Wang, Nicole Xi Zhang, Hongyu He et al.

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have unlocked significant potential to enhance the quality and efficiency of medical care. By introducing a novel way to interact with AI and data through natural language, LLMs offer new opportunities for medical practitioners, patients, and researchers. However, as AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified. These concerns about AI safety have emerged as the most significant obstacles to the adoption of AI in medicine. In response, this review examines emerging risks in AI utilization during the LLM era. First, we explore LLM-specific safety challenges from functional and communication perspectives, addressing issues across data collection, model training, and real-world application. We then consider inherent safety problems shared by all AI systems, along with additional complications introduced by LLMs. Last, we discussed how safety issues of using AI in clinical practice and healthcare system operation would undermine trust among patient, clinicians and the public, and how to build confidence in these systems. By emphasizing the development of safe AI, we believe these technologies can be more rapidly and reliably integrated into everyday medical practice to benefit both patients and clinicians.

Xiaoye Michael Wang, Ali Mazalek, Catherine M. Sabiston et al.

Virtual reality (VR) introduces sensory perturbations that may impact perception and action. The current study was designed to investigate how immersive VR presented through a head-mounted display (HMD) affects perceived functional body size using a passable aperture paradigm. Participants (n=60) performed an action task (sidle through apertures) and a perception task (adjust aperture width until passable without contact) in both physical, unmediated reality (UR) and VR. Results revealed significantly higher action and perceptual thresholds in VR compared to UR. Affordance ratios (perceptual threshold over action threshold) were also higher in VR, indicating that the increase in perceptual thresholds in VR was driven partly by sensorimotor uncertainty, as reflected in the increase in the action thresholds, and partly by perceptual distortions imposed by VR. This perceptual overestimation in VR also persisted as an aftereffect in UR following VR exposure. Geometrical modelling attributed the disproportionate increase in the perceptual threshold in VR primarily to depth compression. This compression, stemming from the vergence-accommodation conflict (VAC), caused the virtual aperture to be perceived as narrower than depicted, thus requiring a wider adjusted aperture. Critically, after mathematically correcting for the VAC's impact on perceived aperture width, the affordance ratios in VR became equivalent to those in UR. These outcomes demonstrate a recovered invariant geometrical scaling, suggesting that perception remained functionally attuned to action capabilities once VAC-induced distortions were accounted for. These findings highlight that VR-induced depth compression systematically alters perceived body-environment relationships, leading to an altered sense of one's functional body size.

Xiaoye Michael Wang, Derek T. Smith, Qin Zhu

Background. Joint range of motion (ROM) is an important quantitative measure for physical therapy. Commonly relying on a goniometer, accurate and reliable ROM measurement requires extensive training and practice. This, in turn, imposes a significant barrier for those who have limited in-person access to healthcare. Objective. The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. Methods. To evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived using this method were compared to those obtained from a marker-based optical motion capture system. Results. Data collected from 25 healthy adults demonstrated that the webcam solution exhibited high test-retest reliability, with substantial to almost perfect intraclass correlation coefficients for most joints. Compared with the marker-based system, the webcam-based system demonstrated substantial to almost perfect inter-rater reliability for some joints, and lower inter-rater reliability for other joints (e.g., shoulder flexion and elbow flexion), which could be attributed to the reduced sensitivity to joint locations at the apex of the movement. Conclusions. The proposed webcam-based method exhibited high test-retest and inter-rater reliability, making it a versatile alternative for existing ROM evaluation methods in clinical practice and the tele-implementation of physical therapy and rehabilitation.

Xiaoye Michael Wang, Catherine M. Sabiston, Timothy N. Welsh

Motion-to-photon latency is inherent in immersive virtual reality (VR) systems and can arise from multiple sensorimotor loops, including view-contingent latency between head movement and display update and interaction latency between hand movement and the virtual effector. Although prior work shows that interaction latency can impair VR performance, it remains unclear whether common spatial, temporal, and efficiency measures reveal the same latency-related disruption. This study addressed this question by experimentally imposing delays between the physical and virtual hands during manual pointing in VR. Participants pointed to targets on a horizontal surface in VR and in the physical environment as an unmediated baseline. In VR, pointing was performed with a virtual hand avatar controlled by a motion capture pipeline, and additional delays (0-500 ms) were imposed between the participant's hand movement and the rendered movement of the virtual hand. Relative to the baseline, performance in VR showed greater endpoint error, longer movement time, greater endpoint variability, and lower throughput. Within VR, added interaction latency further increased endpoint error and variability, reduced throughput, and altered movement time, but these effects followed different profiles: endpoint error increased even at the shortest delays, whereas movement time remained stable at short delays and increased primarily at longer delays. These findings show that interaction latency produces dissociable spatial and temporal consequences in immersive VR, such that endpoint accuracy revealed disruption before movement time or throughput. Thus, latency-sensitive VR interactions cannot be fully evaluated using movement time or efficiency measures alone. Instead, HCI evaluations should assess both spatial and temporal performance, particularly when VR tasks involve visually guided manual actions.

Xiaoxuan Sun, Yue Yao, Xiaoye Wang et al.

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack of accuracy and interpretability of traditional methods in node health status judgment, this paper proposes a health status judgment method based on XGBoost and combines the SHAP method to analyze the interpretability of the model. Through experiments, it is verified that XGBoost has superior performance in processing complex features and nonlinear data of edge computing nodes, especially in capturing the impact of key features (such as response time and power consumption) on node status. SHAP value analysis further reveals the global and local importance of features, so that the model not only has high precision discrimination ability but also can provide intuitive explanations, providing data support for system optimization. Research shows that the combination of AI technology and computer system optimization can not only realize the intelligent monitoring of the health status of edge computing nodes but also provide a scientific basis for dynamic optimization scheduling, resource management and anomaly detection. In the future, with the in-depth development of AI technology, model dynamics, cross-node collaborative optimization and multimodal data fusion will become the focus of research, providing important support for the intelligent evolution of edge computing systems.