Scientists can build on patient health data in research. Here, the development of vaccines, drug optimization, and to provide patients and healthy people with the chance of higher life spans. However, patient health data is higher dimensional data and of a complex nature. With multiple variables per patient such as age, sex, weight, blood pressure, medication level, lab results, and more, a patient health data record can contain up to 1,000 different data columns and include images or test aid monitoring data. Data Quality Assurance Decision-makers are therefore confronted with a massive amount of data and often left with the challenge of bringing it down to a few relevant numbers. Database of patient health data such as medical reports, laboratory results, or vital sign functions needs to be complete, authentic, and precise in their technical terminology. However, sourced raw data can be of bad quality, wrong, or simply missing. Next to this, data inaccuracies can occur during data entry or data coding of patient health data. Corrective algorithms (a.k.a. “cleaning/de-noising algorithms”) are beneficial to improve data reliability and data quality. To do so, recursive sorting to identify false data or filtering functions to match text paragraphs can be applied. Algorithms can also minimize systematic data divergences, complete fragmented data sets, or enhance data for increased numerical stability. Narrow-Sense Based AI, Decision Support Algorithms Today, medical staff and patients can benefit significantly from the combination of human experience and computing power and in a diverse # of medical fields and health care processes. Decision support algorithms are applied to detect characteristic disease patterns in large medical data sets of MRI scans or dermatology skin checks. Diseases such as tumors and melanoma can be identified in early stages, saving many lives. Computing power is also applied to identify and minimize unintended drug-drug interactions (DDIs). Also, training of patients in the usage and dosage of drugs and to avoid hazardous DDIs. In the future, more narrow-sense-based AI applications will contribute even more to secure senior patient health. Evaluation of existing patient health data on local devices with globally interconnected medical knowledge databases is one area. Useful in the provision of local nursing staff, AI-powered predictive alerts can provide more proactive care functions and insights. Another field of application can be lifesaving emergency management. In applying AI, a more secure, vital, and healthy society overall can be achieved, enabling especially the elderly a higher quality of life in old age.