## 3 Results  
### Analysis of Methodological Characteristics of the Selected Literature  
The systematic literature review identified 65 relevant research methods from the selected studies. These methodologies were categorized into four main safety management factors across eight industry sectors, ensuring a coherent and logical grouping of research approaches.  
Categorization Across Sectors: The selected studies were categorized into the following industry sectors, each addressing specific safety management factors:  
Figure 1: Systematic selection process of AI and smart technology applications in safety management.  
1. Construction sector.
2. Industrial safety sector.
3. Disaster and public safety sector.
4. Transport and logistics sector.
5. Energy and power sector.
6. Health sector.
7. Smart home and living sector.
8. Other sectors.  
Safety Management Factors: Each sector addressed one or more of the following safety management factors:  
* Accident prevention and risk management.
* Workplace and facility safety management.
* Quality assurance and reliability management.
* System-based safety enhancement.  
Key Findings:
* Accident prevention and risk management: Predominantly involved AI-driven predictive analytics and sensor-based monitoring systems.
* Workplace and facility safety management: Focused on real-time surveillance using IoT sensors and computer vision technologies.
* Quality assurance and reliability management: Utilized anomaly detection algorithms and ISO/IEC standards to ensure product and process reliability.
* System-based safety enhancement: Emphasized AI-driven automation, smart control systems, and digital twin models to optimize operations and reduce human risk exposure.  
Prevalent Themes and Technologies:
* Machine learning and predictive modeling: Widely used for risk prediction and anomaly detection.
* IoT and sensor networks: Essential for real-time data collection and monitoring.
* Digital twin models: Facilitated simulation and assessment of safety scenarios.
* Human-centric technologies: Enhanced worker safety and efficiency through Augmented Reality and wearable devices.  
The analysis revealed a significant trend towards integrating AI with IoT and Big Data to shift safety management from reactive to proactive approaches.  
Table 1 categorizes the selected studies into eight distinct industry sectors: construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other sectors, highlighting their focus on specific safety management factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sector** & **Frequency** & \multicolumn{2}{c}{**First Author (Year)**} \\ \hline \multirow{6}{*}{Construction} & \multirow{3}{*}{15} & [PERSON], et al. (2012) [11], [PERSON], et al. (2013) [12], [PERSON] (2019) [13], [PERSON], et al. \\  & & (2020) [14], [PERSON], et al. (2021) [15], [PERSON], et al. (2021) [16], [PERSON], et al. \\ \cline{1-1}  & & (2021) [17], [PERSON], et al. (2022) [18], [PERSON], et al. (2023) [19], [PERSON], et al. (2023) [20], \\ \cline{1-1}  & & [PERSON], et al. (2023) [21], [PERSON], et al. (2023) [22], [PERSON], et al. (2023) [23], \\ \cline{1-1}  & & [PERSON], et al. (2024) [24], [PERSON], et al. (2024) [25] \\ \hline \multirow{6}{*}{Industrial Safety} & \multirow{3}{*}{15} & [PERSON], et al. (2011) [26], [PERSON], et al. (2012) [27], [PERSON], et al. (2016) [28], [PERSON], et al. (2018) [29], [PERSON], et al. (2019) [30], [PERSON], et al. (2019) [31], [PERSON], et al. \\ \cline{1-1}  & & (2020) [32], [PERSON], et al. (2020) [33], [PERSON], et al. (2020) [34], [PERSON], et al. (2020) [35], [PERSON] (2021) [36], [PERSON], et al. (2022) [37], [PERSON], et al. (2022) [38], [PERSON], et al. (2022) [39], [PERSON], et al. (2024) [40] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categorization of selected studies across industry sectors.