## 6 Conclusions  
In recent years, artificial intelligence, specifically text mining, has been applied in many areas of knowledge and in systems that humans use daily (chatbots, voice recognition, etc.). The application of these technologies in the legal domain, especially in predicting judicial decisions, has been increasing in recent years. The reviewed papers show, in this ambit, a predominance of the use of text mining techniques, such as Support Vector Machine (SVM), K Nearest Neighbors (K-NN) and Random Forest (RF), over deep learning techniques. These techniques have been applied to predict judicial decisions in various legal domains, such as human rights, divorces, intellectual property, etc., and used to predict the commission of crimes, impute charges or identify the areas of cities with the highest incidence of crime. Although NLP methods are used in the analysis of the legal textual corpus, this trend is due to the shift from ML to DL techniques, which handle a significant corpus and achieve impressive performance. However, the predominance of ML techniques over DL is observed in the results obtained in the works reviewed, i.e., the percentage of ML was 70.6%) and DL was 29.4%. The best result was obtained using the ML algorithm (Multi-Layer perceptron) with 98.7%. This behavior is observed in constitutional law and sentence prediction since the authors preferred using ML techniques such as decision trees, RF, SVM and XGBoost. Furthermore, recent DL techniques were used, such as CNN, LSTM, and their variants, such as BiLSTM, BI-GRU, Attention, and RoBERT. In other areas of law, such as Tax Law, Intellectual Property, and Administrative Law, algorithms such as NB, SVM, XGBoost, decision trees, logistic regression, BERT, Transformers, CNN, and LSTM, were used for the prediction task. Furthermore, some authors proposed modifications to the BERT algorithm, such as ooBERT, LEGAL-BERT, and ft-BERT. Most of the research has been conducted in English. We found that 64% of the works belong to studies carried out in English-speaking countries, 8% in Portuguese and 28% in other languages (such as German, Chinese, Turkish, Spanish, etc.). Very few works of this type have been carried out in Spanish-speaking countries. Applying these prediction techniques in the different legal domains reaches an accuracy of 60%. We believe that it would be interesting to extend this work to other thematic areas and focus on the new technologies that are being developed in legal intelligence for future work. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nro.** & **Reference** & **Corpus Size** & **Lang.** & **Model** & **Repr.** & **Metric** & **Min(\%)** & **Max(\%)** & **Count** \\ \hline
1 & [74] & 695 418 & C & Markov logic & & F-measure & 73.58 & 77.74 & 2018 \\ \hline
2 & [76] & 981112 & F & FlauBERT & TF-IDF, FastText & Accuracy & 74.6 & 93.7 & 2020 \\ \hline
3 & [75] & 100 & I &
\begin{tabular}{c} TSK fuzzy system \\ \end{tabular} & Entropy & Accuracy & 55.3 & 58.8 & 2021 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of ruling decision prediction in Public Law, where C = Chinese, F = French and I = Iranian. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nro.** & **Reference** & **Corpus Size** & **Lang.** & **Model** & **Repr.** & **Metric** & **Min(\%)** & **Max(\%)** & **Year** \\ \hline
9 & [63] & 1.5 M & C & LADAN & TF-IDF & Accuracy & 81.20 & 96.6 & 2020 \\ \hline
10 & [55] & 1562 & P &
\begin{tabular}{c} LR, LDA, KNN, RT, GNB, SVM \\ \end{tabular} & TF-IDF & F-measure & 78 & 98 & 2020 \\ \hline
11 & [64] & 503 & F &
\begin{tabular}{c} CastermBERT, BiLSTM-CRF \\ \end{tabular} & F-measure & 81.43 & 100 & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
**Author Contributions:** Conceptualization, O.A.A.F., M.N.-d.-P. and H.A.-S.; methodology, O.A.A.F., M.N.-d.-P. and H.A.-S.; validation, M.N.-d.-P., O.A.A.F. and H.A.-S.; formal analysis, O.A.A.F., M.N.-d.-P. and H.A.-S.; investigation, O.A.A.F., M.N.-d.-P. and H.A.-S.; resources, O.A.A.F.