**5. Conclusion**

The supervised and semi-supervised machine learning techniques are applied in the main three PV group activities; detection of adverse drug reactions (ADRs) and signal detection, individual case safety reports (ICSRs) identification, and ADRs prediction. Furthermore, it helps in analyzing large data sources, such as social media and literature, to predict and detect ADRs, accordingly, it complements the drawbacks of spontaneous reporting. Moreover, ML techniques are efficient in terms of accuracy and saving time when compared to human experts.

#### **Knowledge gaps**

The supervised learning technique is currently used in PV activities, which has a problem with the scarcity of labeled data [16], so the first knowledge gap is how to apply the unsupervised technique in PV activities.

The second knowledge gap is that PV activities are legally regulated [22], therefore, a regulation should be developed to manage the risk of false-negative detected results.

*Machine Learning Applications in Pharmacovigilance: Scoping Review DOI: http://dx.doi.org/10.5772/intechopen.107290*

The third knowledge gap: further research is needed to assess the attitude, knowledge, and practice of PV personnel regarding the applicability of the ML techniques in PV daily practice.
