# Applications of Machine Learning Algorithms in Geriatrics
## 5 Discussions
Few studies follow patients in the long term, limiting the models' ability to provide predictions over time. A future research direction should address the longitudinal issues of research in relation to ML. 4. Integration into real clinical practice represents another future direction for research, given that, although studies report good performance on limited datasets, in practice, they may encounter situations that were not considered in the datasets used for research. 5. Ethics and data protection in geriatrics is an extremely sensitive topic, especially in the context of AI usage, which raises a series of ethical questions related to consent, data access, algorithmic bias, and equity. Considering these gaps, the research directions recommended by the authors of this paper are as follows:  
1. The creation of standardized open-access databases in which the collected and labeled data are explained, so that as many studies as possible related to ML can be provided in the geriatric field. 2. Studying the explainability of algorithms through methods such as SHAP or Locally Interpretable Model-Agnostic Explanations (LIME), XAI, or other ML algorithms. 3. The implementation of prospective studies that validate the models over time and track the impact on patients' quality of life, thus extending the studies generated by ML algorithms over a span of decades. 4. Interdisciplinary collaborations between engineers, programmers, geriatricians, psychologists, and medical ethics experts. 5. Designing integrated AI systems with interfaces that can be used by medical staff without requiring expertise in programming or data science. Synthesizing the results analyzed in this paper, it is found that ML algorithms impact the way health policies for the elderly are approached. Thus, ML models triage patients in emergency units in the context of healthcare system overload. With the help of these algorithms, the monitoring of patients with chronic diseases is ensured through wearable devices and sensors connected to these devices [91]. Additionally, these algorithms allocate medical resources, and, by these resources, the authors refer to beds, personnel, and equipment, with allocation being based on predictions of the patients' health status evolution. Additionally, these algorithms personalize patient treatment by taking into account historical data, laboratory tests, lifestyle, and patient comorbidities. For these benefits to materialize, ML algorithms need to be integrated into the national electronic health systems by developing clinical guidelines that include the use of these algorithms. Additionally, medical personnel require proper training in interpreting results obtained through AI. For all these things to materialize, it is necessary that we ensure a legal and ethical framework regarding data protection. Some ML applications in the literature reviewed have demonstrated quantifiable clinical benefits, such as reducing the hospital stay duration. For example, in the study conducted by [PERSON] et al. [27] on geriatric patients with hip fractures, the XGBoost model predicted the length of hospital stay with an accuracy of 92.4% and an AUC of 98.8%. These results optimize the allocation of medical resources. Another example is provided by [PERSON] et al. [42], who correlated ML scores with the probability of prolonged hospitalization. This approach supports early intervention, which prevents complications from arising. Such results as ML models impact clinical workflows at the geriatric level. In the field of geriatrics, some patients suffer from cognitive impairments, which render them unable to provide informed consent regarding the use of AI. For these reasons, the patient or their relatives need to be informed that personal data will be processed through ML models. On the other hand, algorithms do not replace human clinical decision-making. These models amplify unintentional discrimination if they are trained on historical data with bias, such as treatment inequalities based on age or gender.