# Applications of Machine Learning Algorithms in Geriatrics
## 5 Discussions
The lack of transparency in the models, as the algorithms represent black boxes, reduces the trust of the doctor or the patient in the final decision. For this reason, it is important to mention that the professional is the one who must always make the final decision in the case of ML algorithms. Regarding patient personal data, in Europe, the General Data Protection Regulation (GDPR) restricts the processing of sensitive data, especially if it is of a medical nature. ML models that use such data must adhere to the principle of data minimization, provide explanations for algorithmic decisions through the right to an explanation according to Article 22 of GDPR, and maintain their absolute confidentiality. In addition to the GDPR, the AI Act is being adopted, representing the first legal framework for AI that classifies medical applications as high-risk. According to this act, ML applications in geriatrics must be transparent, allow for auditability, and be subject to continuous clinical validation. Given these constraints, the authors' ethical recommendations include explaining the algorithms to doctors, ensuring that decision support systems have logs and a decision history in a format accessible to the doctor, accompanying each ML model with formal ethical evaluations, such as Clinical Ethics Committees, and, last but not least, obtaining the patient's or legal representative's consent, who must be informed about how the ML algorithms will process their data. GDPR-integrated ML applications intended for geriatrics should benefit from technical measures that enable developers of these algorithms to pseudonymize patient data, making it impossible to identify patients based on the analyzed data. Another provision should be the limitation of access to patient datasets. These should only be accessed by authorized researchers, as determined by permission control. The periodic auditing of algorithmic decisions should be another measure with which to identify potential forms of bias within the context of GDPR compliance. Another norm should be ensuring the right to an explanation under the GDPR. Methods like SHAP or LIME provide medical staff with access to the algorithm's decision-making reasoning. This is associated with the transparency of the algorithm's decision-making and should be a mandatory norm in the development of ML algorithms. Moreover, obtaining consent accompanied by a description of how the algorithms process data should be another mandatory norm in the development of these algorithms. In this systematic review, both internal and external validity were established to ensure the importance of the conclusions. In this paper, a study selection methodology and a content analysis conducted by the authors based on their own expertise were used. In this section, an explicit discussion will be conducted on the validity dimension, highlighting the understanding of the limitations and generalizability of the results. Internal validity is associated with the logical coherence between the objectives of the paper, the inclusion criteria, the analysis method, and the conclusions drawn. In this study, internal validity is due to the use of a search strategy defined according to WOS rules by including terms, bulleted lists, and the temporal delimitations of 1 January 2020 to 31 May 2025. The selection of articles is based on eligibility criteria and dual analytical methods, qualitative and quantitative. The analysis of performance metrics ensures the objectivity of evaluations in relation to the performance of ML models used in the geriatric context. External validity refers to the extent to which conclusions generalize to external contexts; yet, it is limited by factors such as the studies' origins in developed countries with an advanced IT infrastructure or the frequently heterogeneous data that do not allow for the reproducibility of models due to the lack of common standardization. The implementation of ML algorithms in geriatric faces an obstacle in the form of low trust from medical staff in black box models.