Background: Systematic literature reviews (SLRs) build the foundation for evidence synthesis, but they are exceptionally demanding in terms of time and resources. While recent advances in artificial intelligence (AI), particularly large language models, offer the potential to accelerate this process, their use introduces challenges to transparency and reproducibility. Reporting guidelines such as the PRISMA-AI (Preferred Reporting Items for Systematic Reviews and Meta-Analyses–Artificial Intelligence Extension) primarily focus on AI as a subject of research, not as a tool in the review process itself. Objective: To address the gap in reporting standards, this study aimed to develop and propose a discipline-agnostic checklist extension to the PRISMA 2020 statement. The goal was to ensure transparent reporting when AI is used as a methodological tool in evidence synthesis, fostering trust in the next generation of SLRs. Methods: The proposed checklist, named PRISMA-trAIce (PRISMA–Transparent Reporting of Artificial Intelligence in Comprehensive Evidence Synthesis), was developed through a systematic process. We conducted a literature search to identify established, consensus-based AI reporting guidelines (eg, CONSORT-AI [Consolidated Standards of Reporting Trials–Artificial Intelligence] and TRIPOD-AI [Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis–Artificial Intelligence]). Relevant items from these frameworks were extracted, analyzed, and thematically synthesized to form a modular checklist that integrated with the PRISMA 2020 structure. Results: The primary result of this work is the PRISMA-trAIce checklist, a comprehensive set of reporting items designed to document the use of AI in SLRs. The checklist covers the entire structure of an SLR, from title and abstract to methods and discussion, and includes specific items for identifying AI tools, describing human-AI interaction, reporting performance evaluation, and discussing limitations. Conclusions: PRISMA-trAIce establishes an important framework to improve the transparency and methodological integrity of AI-assisted systematic reviews, enhancing the trust required for the responsible application of AI-assisted systematic reviews in evidence synthesis. We present this work as a foundational proposal, explicitly inviting the scientific community to join an open science process of consensus building. Through this collaborative refinement, we aim to evolve PRISMA-trAIce into a formally endorsed guideline, thereby ensuring the collective validation and scientific rigor of future AI-driven research.

This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress detection in manual assembly via HAR-based methods, as well as visual tool-recognition approaches. A novel online tool-recognition pipeline for handheld tools is introduced, utilizing a two-stage approach. First, a Region Of Interest (ROI) was extracted by determining the wrist position using skeletal data. Afterward, this ROI was cropped, and the tool located within this ROI was classified. This pipeline enabled several algorithms for object recognition and demonstrated the generalizability of our approach. An extensive training dataset for tool-recognition purposes is presented, which was evaluated with two image-classification approaches. An offline pipeline evaluation was performed with twelve tool classes. Additionally, various online tests were conducted covering different aspects of this vision application, such as two assembly scenarios, unknown instances of known classes, as well as challenging backgrounds. The introduced pipeline was competitive with other approaches regarding prediction accuracy, robustness, diversity, extendability/flexibility, and online capability.

Globally interconnecting machines, processes, and resources driven by exploring and advancing new technologies defined Industry 4.0 (I4.0), resulting in, e.g., Cyber-Physical Production Systems (CPPS). The aircraft industry particularly struggled with transforming production and Maintenance, Repair, and Overhaul (MRO) processes, replacing humans with machines and automating as well as digitalizing significant parts of their value- and non-value-adding activities. However, in the face of current social and environmental challenges, future industries will need to shift from purely technology-driven to value-driven, working sustainably with resources, including human capital. Together, these approaches constitute the idea of Industry 5.0 (I5.0). On the one hand, the aviation industry faces the challenge that even I4.0 concepts and technologies are not yet fully exploited or implemented. On the other hand, due to the specific characteristics of aircraft production and MRO as well as the environmental impact of the product, a tremendous potential arises regarding placing human well-being back into the center of adding value and decreasing environmental footprint while building an industry that is resilient and fortified against disruptions of this era. In line with the I5.0 terminology, in this work, we outline the challenges and opportunities of integrating I5.0 principles into the aircraft production and MRO industries, focusing specifically on the scope of selected use cases.

Assistance systems utilize a broad range of technologies to provide information and guidance to workers in manufacturing. The use of light projectors, as of today, has seldom been catalogued in the relevant literature, and the implementation of such is yet to be found in production environments. However, light projectors may offer a cost effective enhancement for production processes, especially within the context of large-scale workpieces. Of the pertaining literature, only one calibration algorithm is currently considered applicable, thus acting as a framework of motivation for this paper. A novel calibration algorithm based on Newton’s method is presented and validated in conjunction with a proof-of-concept demonstration of the resulting accuracy, as well as the integration of such into an interface based on Node-RED, with MQTT as the main protocol.