Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review

IntroductionArtificial intelligence (AI) technologies are increasingly applied to empower clinical decision support systems (CDSS), providing patient-specific recommendations to improve clinical work. Equally important to technical advancement is human, social, and contextual factors that impact the successful implementation and user adoption of AI-empowered CDSS (AI-CDSS). With the growing interest in human-centered design and evaluation of such tools, it is critical to synthesize the knowledge and experiences reported in prior work and shed light on future work.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review to gain an in-depth understanding of how AI-empowered CDSS was used, designed, and evaluated, and how clinician users perceived such systems. We performed literature search in five databases for articles published between the years 2011 and 2022. A total of 19874 articles were retrieved and screened, with 20 articles included for in-depth analysis.ResultsThe reviewed studies assessed different aspects of AI-CDSS, including effectiveness (e.g., improved patient evaluation and work efficiency), user needs (e.g., informational and technological needs), user experience (e.g., satisfaction, trust, usability, workload, and understandability), and other dimensions (e.g., the impact of AI-CDSS on workflow and patient-provider relationship). Despite the promising nature of AI-CDSS, our findings highlighted six major challenges of implementing such systems, including technical limitation, workflow misalignment, attitudinal barriers, informational barriers, usability issues, and environmental barriers. These sociotechnical challenges prevent the effective use of AI-based CDSS interventions in clinical settings.DiscussionOur study highlights the paucity of studies examining the user needs, perceptions, and experiences of AI-CDSS. Based on the findings, we discuss design implications and future research directions

The Effects and Vertical Bearing Capacity of Two Jacked Model Piles in Sand

The effects and vertical bearing capacity of two jacked piles in sand are still not well understood, and the mechanism of the adjacent pile’s uplift caused by the jacking pile in a double pile system is especially unclear, but these facets are important to the stability of the jacked pile. In this paper, a series of tests is performed on jacked model piles in sand, where in the influences of the pile length and the driving pile’s speed on the effects and vertical bearing capacity of two jacked piles were studied. The results revealed that the effects and vertical bearing capacity of the two jacked piles were mainly in relation to pile length and influenced by the driving speed. The horizontal displacement of the top of the first jacking pile during the installation of the post-jacking pile was caused by the difference in the stress state of the first jacking pile between the side of the pile’s face and its back side, in which the uplift displacement of the first jacking pile was also involved. The radial stress of the pile increased nonlinearly with the depth under different pile lengths and gradually converged to the passive earth pressure. The ultimate capacity of the double pile is approximately twice that of a single pile, and the ratio of the ultimate capacity of a single pile to the final jacking pressure was approximately 1.04

Towards high activity of hydrogen production from ammonia borane over efficient non-noble Ni5P4 catalyst

Catalytic hydrolysis of ammonia borane has tremendous potential as an energy-efficient approach to supply hydrogen for energy vehicles and portable electronic devices. Herein, DFT calculation is first performed on electronic properties of Ni2P and Ni5P4 nanocatalysts. It is found that more electrons are transferred from Ni to P for Ni5P4, indicating that Ni5P4 may show superior performance based on the electron effect. Therefore, Ni2P and Ni5P4 with high purity are synthesized by the phase-controlled thermal decomposition approach. Gratifyingly, the Ni5P4 catalyst exhibits the as-expected better catalytic activity than that of Ni2P catalyst. It also shows low activation energy and good stability. Furthermore, the structures and morphologies of both catalysts are characterized by multi-techniques such as XRD, HRTEM and XPS. The better performance could be ascribed to the higher positive charge of Ni together with the stronger ensemble effect of P. The insights sheds new light on the design of efficient NiP catalysts for hydrogen generation