Leveraging Ai And Iot For Targeted Nanomedicine: A New Era In Precision Medicine
DOI:
https://doi.org/10.63682/jns.v14i28S.6569Keywords:
AI, IoT, NM, PM, PS, HO, QRAbstract
Background: The combination of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in nanomedicine allows the Anglo to transform precision medicine through better treatment, patient details, and healthcare systems. However, it is still questionable as to how exactly the application of these technologies influences patient satisfaction as well as the efficiency of treatment.
Objective: This work will also seek to find out how the application of AI and IoT technologies in nanomedicine has impacted different health outcomes, patient satisfaction, health improvement, and the perceived usefulness of AI in future healthcare, among others.
Methods: A descriptive, online self-administered questionnaire was distributed to healthcare professionals, patients, and technology users engaged in developing nanomedicine with the aid of AI. An online survey in the form of a structured questionnaire was administered to obtain information on the primary research variables: satisfaction with technology, health improvement, trust in an AI, and comfort with AI systems. A descriptive analysis of frequency and distribution, regression analysis test, normality, and reliability coefficients were used to analyze the data.
Results: These findings have suggested positive and negative perceptions of AI and IoT technologies in nanomedicine. When comparing the level of satisfaction with the usage of different technologies in healthcare with self-reported health benefits, the study revealed a relatively low correlation (R² = -0. 088), which means that there should be other factors that determine such results. The Shapiro-Wilk test further confirmed that the data distribution was non-normal in several variables, hence the variability in the users' experiences. Furthermore, there was a significant correlation between trust in AI for healthcare decisions and the relevance of AI in healthcare. However, Cronbach's alpha amounted to - 0. 13 raised concerns about relatively lower internal reliability coefficients for some survey questions.
Conclusions: These innovations, through AI and IoT, hold significant promise for nanomedicine and precision health, but the current study implies a significant gap between technological possibilities and patient receptiveness. That is why trust and experience in their usage are the key factors for the proliferation of such technologies among people. In addition, there is a need to enhance the reliability of survey instruments in subsequent research activities to reduce inaccuracies where they occur. Future studies should investigate more facets of AI use in nanomedicine and elaborate on the methods to measure the enhancement of the health sector outcomes due to AI.
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