As a result of the significant advances in the field of medicine and technology worldwide, health centers, hospitals and clinical and scientific research institutes have called this period the “era of artificial intelligence (AI)”, the era of AI has brought numerous advances to the field of medicine, especially in the field of patient data security. The use of AI has gained widespread notoriety in the health sector through promising results regarding the protection of patient data, as well as guaranteeing the privacy of personal information .
An example regarding the use of AI refers to the implementation of machine learning (ML) algorithms. These algorithms analyze large amounts of data and identify patterns. In the context of protecting patients’ personal data, models have been created to detect and respond early to threats in the hospital system, consequently reducing the risk of data breaches and exposure of confidential information .
Essentially, the ML models applied to data protection are trained for:
– Detection of an anomaly in data: the structure of clinical, laboratory and image data present patterns that are used for training ML models to identify through ruptures in potential violations and to elaborate strategies for prevention and early response to possible threats .
– Prevention of human errors: the patterns observed in medical data are also widely used for record-keeping, i.e., ML models can be trained to continuously check patient information and generate reports, as well as show inconsistencies about the record, resulting in data recording from reliable sources, minimizing human manipulation and data exposure .
Another AI application in patient data security has been the use of natural language (NL) processing technologies (NLP) and natural language generation (NLG), these technologies allow healthcare professionals to analyze patient data and generate concise, accurate reports. and easy to understand. This reduces the risk of human error and ensures that patient data is managed responsibly and securely [1-3]. The recent advances regarding to the use of natural language generation is:
– Medical conversation analytics: An example of using natural language to protect clinical data is medical conversation analytics. Natural language processing algorithms can be trained to analyze conversations between healthcare professionals and patients, detecting confidential or sensitive information that may have been accidentally mentioned and removing it before it is recorded or shared .
– Automated Clinical summaries: NL processing algorithms can be trained to read clinical reports and extract relevant information, creating automated summaries that highlight only the important information for medical staff, reducing the risk of exposing sensitive information to unauthorized individuals .
In summary, the AI era has brought numerous advances to the field of medicine, and its use in patient data security is particularly noteworthy. Implementing AI technologies such as machine learning algorithms, NLP and NLG have greatly improved patient data security and privacy. Further research and development in this area will likely continue to produce new and innovative solutions to ensure patient data security for years to come.
. Eysenbach G.. The potential of artificial intelligence in healthcare. The Lancet Digital Health, 2018; 1(1) e1-e6. 2.
. Al-Janabi H. Lee J. (2020). Artificial intelligence in medical imaging: A review of current applications and future prospects. Journal of medical systems, 44(10), 467.
 Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018; 2: 35.
 Lin D, Xiong J, Liu C, Zhao L, Li Z, et al. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health. 2021 Aug;3(8):e486-e495.