Page 81 - Petelin, Ana. 2024. Ed. Zdravje delovno aktivnih in starejših odraslih | Health of the Working-Age and Older Adults. Zbornik prispevkov z recenzijo | Proceedings. Koper: University of Primorska Press

rodegeneration, which is distinct from the expected memory impairment com-
mon in the elderly (Zlatanova, 2023). Wearable devices, smartphones, etc., are
one of the options to maintain health, already perceived as medical devices that
can monitor health-related information, for example by measuring vital signs,
physical fitness, dietary calories, exercise (Giordano et al., 2021). With the use
of AI, the large amount of data obtained can be analysed (similarities and dif-
ferences) to enable the planning of diets, breaking of bad habits for instance in
diabetes maintenance or even prevention. By detecting the signs of vision loss
as a consequence of diabetes, timely treatment of the underlying disease and its
consequences could be provided (Zlatanova, 2023). At the same time, individ-
uals with certain health problems that eventually develop into complications
could be identified (Necher et al., 2023). It can be concluded that the collection
of a large amount of data and its analysis using AI support allows for timely di-
agnosis and subsequent treatment; this approach can also act as a preventive
measure, as by abandoning certain lifestyle habits, potential health problems
can be avoided. However, it is to the detriment of the individual and the health 81
system that this data remains isolated and often untapped (lack data accessibil-
ity and sometimes questionable quality, lack of incentives), rather than being
integrated within existing processes (Gopal et al., 2019).

Quality Clinical Diagnosis, Decision-Making and Personalised Patient
Care
Using AI, all healthcare professionals and patients can easily access state-of-
the-art diagnostics, treatments, development of therapies tailored to the needs
of the individual (Shah et al., 2019). In the future, AI tools will be refined, soft-
ware will be upgraded and databases will be improved (Watson, 2024).
The types of diseases that AI communities are most concerned with are the use of artificial intelligence in the field of health for working-age adults and older adults
cancer, diseases of the nervous system and cardiovascular diseases. This se-
lection is not surprising as all three diseases are major causes of death. Accu-
rate and early diagnoses can be achieved with AI support, enabling improve-
ments in the analysis processes of structured data and unstructured records;
the latter requiring conversion into a machine-readable electronic medical re-
cord (Jiang et al., 2017, Topol, 2019). AI algorithms can help reduce potential
human cognitive biases to aid clinical decision making through accurate di-
agnosis (Dilsizian and Siegel, 2014; Giordano, et al., 2021; Brown et al., 2023).
Indeed, research has shown a synergistic effect: when physician and AI work
together, better outcomes are achieved (He et al., 2019). Especially in the diag-
nostic phase and later on, AI also contributes to clinical decisions by process-
ing written information, which, together with patient data and the processing
of existing professional and scientific medical literature, helps in the diagnosis
and recommendations on treatment options (Hernandez et al., 2017; Giordano
et al., 2021).
At the same time, AI supports the implementation of personalised pa-
tient care and monitoring (Chen and Decary, 2020). The digitisation of medical