## 8 Conclusions and Future Work  
Training AI models capable of providing accurate and timely evaluations for the priority of emerging issues can offer significant value in the health and safety industry. Focusing on OSH domain knowledge, the presented research described a pipeline of tasks that models, refines and analyzes unstructured text from safety reports. A multimodal approach was explained and tested for producing valid risk ratings for reports and effectively serving two different classification themes for organising them. Such models can drive the appropriate tools for the decision support and improved performance of inexperienced safety engineers, while also streamlining the assessment process for experienced ones. Offering insights and recommendations for the classification of safety reports can directly impact inspection performance in any workplace or shop-floor, where hundreds or thousand of issues require immediate attention and handling. Allowing non-experts such as clients, employees or the general public to contribute tp a feedback loop that validates and enriches the above knowledge is a major step towards building confidence and certainty for the underlying models. The discussed models will support the applications and services of the Safety4 ALL platform, delivering a collective awareness platform for OSH reports. Investing in crowdsourcing and mobile technologies, the platform will drive the collection, review, enrichment and classification of reports, ensuring the required volume of data that can consistently update and retrain the models. Statistics from each category will support tasks that plan appropriate training for employees and users of the site, record the performance of different types of equipment and identify major sources of hazards in each site. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target** & **Modality** & **Text Encoder** & **Visual Encoder** & **Fusion Method** & **Accuracy (\%)** & \(\Delta\uparrow\)**Unimodal** \\ \hline \multirow{8}{*}{LSSue Source} & \(\mathcal{V}\) & - & ResNet18 & - & 60.41 & - \\  & \(\mathcal{V}\) & - & ViT & - & 63.84 & - \\  & \(\mathcal{T}\) & Greek BERT & - & - & 73.74 & - \\  & \(\mathcal{T}\) & GR-Electra & - & - & 72 & - \\ \cline{2-6}  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ResNet18 & cat & 69.47 & \(-\)4.27 \\  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ResNet18 & CLSS & **73.88** & 0.14 \\  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ViT & cat & 71.17 & \(-\)2.57 \\  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ViT & CLSS & **74.15** & 0.41 \\  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ResNet18 & cat & 67.95 & \(-\)4.05 \\  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ResNet18 & CLSS & 68.28 & \(-\)3.72 \\  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ViT & cat & 67.43 & \(-\)4.57 \\  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ViT & CLSS & **73.88** & 1.88 \\ \cline{2-6}  & \multicolumn{4}{c}{Random Baseline} & \multicolumn{4}{c}{59.03} \\ \hline \multirow{8}{*}{Category} & \(\mathcal{V}\) & - & ResNet18 & - & 50.46 & - \\  & \(\mathcal{V}\) & - & ViT & - & 55.67 & - \\ \cline{1-1}  & \(\mathcal{T}\) & Greek BERT & - & - & 69.53 & - \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ResNet18 & cat & 57.43 & \(-\)12.1 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ResNet18 & CLSS & **70.2** & 0.67 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ViT & cat & 62.41 & \(-\)7.12 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ViT & CLSS & **70.59** & 1.06 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ResNet18 & cat & 64.23 & \(-\)2.57 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ResNet18 & CLSS & 66.42 & \(-\)0.38 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ViT & cat & 65.69 & \(-\)1.11 \\ \cline{1-1}  & \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ViT & CLSS & 65.15 & \(-\)1.65 \\ \hline \multicolumn{6}{c}{Random Baseline} & \multicolumn{4}{c}{43.46} \\ \hline \hline \end{tabular}
\end{table}