[Translated article] Introducing artificial intelligence to hospital pharmacy departments.

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming.

Approaching artificial intelligence to Hospital Pharmacy.

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming.

Clinical experience with orphan drugs for rare metabolic diseases.

Introduction: Inborn errors of metabolism are a highly heterogeneous group of orphan diseases. Diet therapy and enzyme and coenzyme replacement are the most frequently used treatment. There are few patients and published studies about inborn errors of metabolism.

[Clinical experience with orphan drugs for rare metabolic diseases].

Introduction: Inborn errors of metabolism are a highly heterogeneous group of orphan diseases. Diet therapy and enzyme and coenzyme replacement are the most frequently used treatment. There are few patients and published studies about inborn errors of metabolism.

Prevalence of the consumption of anticholinergic drugs in HIV patients.

Objective: To analyse anticholinergic agent consumption in HIV patients 50  years or older; to determine anticholinergic risk using the ACB and ARS scales;  and to determine if these patients use any type of benzodiazepine.

Method: A descriptive observational study of 256 HIV patients 50 years or  older.

Results: 73.