Page 82 - 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

records, the proliferation of smartphones and fitness monitoring are enabling
access to digital technologies and, at the same time, the use of AI for patient
monitoring, for instance patient sleep patterns, blood pressure, heart rate, etc.
can be obtained in ways that have not been available before (Reddy et al., 2019).
The virtual use of the listed assistants can also help in communicating health
status, especially for elderly people outside the hospital setting, use of medica-
tion, analysis of patients’ health status (Seibert et al., 2021). By obtaining pa-
tient information and analysing data, AI can also help reduce queues in emer-
gency clinics (Yousefi et al., 2018). The potential of generative AI support in
health and patient care for healthcare diagnoses, planning goals and interven-
tions should also be mentioned. However, it has been shown that AI is still not
specific enough and requires human critical reflection and verification (Gosak
et al., 2024).
The use of robots in patient care, especially for the elderly, has become a
82 major focus, helping to reduce health risks. Robots can be used to remind the
elderly to perform regular activities, take medication, and guide them in their
zdravje delovno aktivnih in starejših odraslih | health of working-age and older adults
environment (Reddy et al., 2019).
However powerful AI techniques may be, the process of patient care be-
gins and ends with clinical activities (Jiang et al., 2017). A similar conclusion
was reached by researchers Laukka et al. (2022) in interviews conducted with
nurse managers and developers of digital services. The interviewees felt that
specialised healthcare will have several positive implications for work, servic-
es and organisation.

Potential Roles of AI in Clinical Settings and a Look to the Future
The application of AI in clinical settings does not take place only in oncology,
cardiology and neurology, but also in other areas of healthcare (He et al., 2019).
AI technology can aid triage or screening by selecting priority radiographs
based on the most probable disease (Tang et al, 2018). It can help in identify-
ing risks to eyesight, differentiating between patients who urgently need face-
to-face examination and those who are not at risk (triage), diagnosing disease,
quantifying vascular stenosis in cardiac imaging (Pelcyger, 2017; De Fauw et
al., 2018; Kermany et al., 2018; Giordano et al., 2021; Brown et al., 2023). In the
future, AI technologies will be most rapidly applied in the fields of radiology,
ophthalmology, dermatology and pathology, mainly due to their powerful im-
aging and visual components with the possibility of automated analysis. How-
ever, according to some researchers (He et al., 2019), internal medicine and sur-
gery will be somewhat later entrants into the AI world; the former because it
requires the integration of different types of data and the latter because of the
procedural components.
The integration of AI into the clinical setting will require the alignment
of healthcare workers and developers regarding the goals. The future will also