Page 31 - IMDR Journal 2025

Research Article
responsive and tailored to individual needs — something health/ai-in-healthcare
traditional systems often fail to do.
Health Insurance Portability and Accountability Act
However, there are still some real challenges. Many people (HIPAA) (2022). Privacy and security considerations in AI-
are unsure if their health data is safe, and rightly so. Without d r i v e n h e a l t h c a r e . R e t r i e v e d f r o m
strong privacy protections, sensitive information could fall https://www.hhs.gov/hipaa/index.html
into the wrong hands. There's also concern about fairness — IBM Watson Health (2020). Using AI for personalized
some AI systems don’t perform equally well across all diabetes care: Innovations and challenges. Retrieved from
population groups, especially when the data used to train https://www.ibm.com/watson-health/ai-in-healthcare
them isn’t diverse. These biases can lead to unequal care.
And on top of that, there are still no clear global rules or International Diabetes Federation (2021). IDF Diabetes
standards for using AI in healthcare, which slows down its A t l a s , 1 0 t h E d i t i o n . R e t r i e v e d f r o m
wider adoption. https://www.diabetesatlas.org
Khan, S. S., Ning, H., Wilkins, J. T., & Lloyd-Jones, D. M.
To move forward, we need to address these issues.
Healthcare workers must be trained to understand and use AI (2019). Association of AI-driven predictive analytics with
tools confidently. AI developers, hospitals, and regulators diabetes risk reduction: A population-based study. The
need to work together to make sure new technologies fit into L a n c e t D i g i t a l H e a l t h , 1 ( 2 ) , e 7 9 - e 8 9 .
real-world healthcare settings. We also need better laws and https://doi.org/10.1016/S2589-7500(19)30028-6
policies to protect patient data and ensure ethical use. At the Lee, H. J., Kim, J., & Kim, J. H. (2020). Artificial
same time, research should focus on using AI to prevent intelligence in diabetic management: Current applications
diabetes in high-risk individuals before it even starts. This and future perspectives. Diabetes & Metabolism Journal,
would not only improve health outcomes but also reduce the 44(6), 819-839. https://doi.org/10.4093/dmj.2020.0201
strain on healthcare systems. Livongo (2020). AI-based remote monitoring for diabetes
Looking ahead, AI holds the power to make diabetes care management: A digital health success story. Retrieved from
smarter, faster, and more affordable - but only if we build https://www.livongo.com
trust, ensure fairness, and keep people at the heart of every Medtronic (2021). MiniMed™ 670G System: AI-driven
decision. By using AI responsibly and transparently, we can i n s u l i n d o s i n g t e c h n o l o g y. R e t r i e v e d f r o m
create a healthcare future that’s not only high-tech, but also https://www.medtronicdiabetes.com/products/minimed-
deeply human. 670g-insulin-pump-system
Microsoft AI for Health (2021). Applying AI to tackle
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