ChatGPT is a highly advanced AI language model that has gained widespread popularity. It is trained to understand and generate human language and is used in various applications, including automated customer service, chatbots, and content generation. While it has the potential to offer many benefits, there are also concerns about its potential for misuse, particularly in relation to providing inappropriate or harmful safety-related information. To explore ChatGPT's (specifically version 3.5) capabilities in providing safety-related advice, a multidisciplinary consortium of experts was formed to analyse nine cases across different safety domains: using mobile phones while driving, supervising children around water, crowd management guidelines, precautions to prevent falls in older people, air pollution when exercising, intervening when a colleague is distressed, managing job demands to prevent burnout, protecting personal data in fitness apps, and fatigue when operating heavy machinery. The experts concluded that there is potential for significant risks when using ChatGPT as a source of information and advice for safety-related issues. ChatGPT provided incorrect or potentially harmful statements and emphasised individual responsibility, potentially leading to ecological fallacy. The study highlights the need for caution when using ChatGPT for safety-related information and expert verification, as well as the need for ethical considerations and safeguards to ensure users understand the limitations and receive appropriate advice, especially in low- and middle-income countries. The results of this investigation serve as a reminder that while AI technology continues to advance, caution must be exercised to ensure that its applications do not pose a threat to public safety.@en

Road users in low- and middle-income countries (LMICs) are overrepresented in road trauma statistics. Despite the relative success of many high-income countries (HICs) in reducing deaths on their roads, not much tangible progress has been made in LMICs. Also, on the research front, the vast majority of road safety knowledge has been emerging from institutes of HICs. Considering significant differences in driving culture, legislation, and traffic law enforcement between LMICs and HICs, it seems essential that research on road safety within LMICs intensifies beyond the existing rate to produce the much-needed local knowledge and to develop initiatives that meet their safety needs and upgrade their practices. To facilitate this, here, the landscape and temporal trends of road safety research in LMICs are analysed while contrasting them with those of the general scholarly literature on road safety. It is estimated that slightly less than 10% of the road safety research has been undertaken in the contexts of LMICs, which is extremely disproportionate considering the fact that most road traffic deaths and injuries occur in LMICs. Questionnaire-based research on socio-psychological aspects of driving, cycling, and walking as well as statistical modelling of road crash data seem to have made up the dominant focus of LMIC researchers within the recent years. Areas of road safety research that are underrepresented in LMIC studies are also identified in this work. Patterns of authorship and co-authorship in LMIC studies are also analysed at the level of countries, organisations, and authors. It is hoped that this effort can contribute to further invigoration of road safety research in LMICs and to highlighting the current knowledge gaps, while also giving better recognition to active road safety researchers of LMICs, and thereby, prompting more international collaborations in this domain.@en

Introduction: Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature. Method: Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles. Results: The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity ” and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature. Conclusions: By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.@en