Research

How did your initial exploration of the scholarly conversation lead to your final research question/project goal?

While exploring early studies, I noticed most research on AI in diabetes focused on theoretical models rather than real-world healthcare settings. This gap helped shape my research question, which targets AI’s practical accuracy and efficiency in diagnosing diabetes.

How did your review of the methods used by scholars in the field inform your selection of a research method/process that is aligned with your research question/project goal?

Most scholars used either data analysis or clinical trials. I noticed a lack of perspectives from practicing doctors, so I chose a mixed-methods approach — combining article analysis and surveys — to capture both data and human experience.

How did the choices you made when designing or implementing your research method impact your research process?

Using both surveys and content analysis gave me a balanced view of real-world experiences and published research, which deepened my understanding and strengthened the credibility of my conclusions.

How did you determine which results generated by your research method were most important in informing your new understanding?

I prioritized results that appeared repeatedly across both the literature and survey responses — specifically those that highlighted AI’s strengths in early detection and limitations in diverse patient populations.

How does your new understanding address a gap in the scholarly conversation?

My findings connect theoretical AI model performance to real-world clinical feedback, offering insights into how AI actually functions in practice — something many past studies lacked.

How did the limitations of your method or data influence your new understanding?

My limited sample size and regional focus highlighted the need for larger, more diverse studies, which shaped my understanding of AI’s uneven implementation in healthcare systems.

What are the real-world implications or consequences related to your findings?

AI has the potential to improve early diabetes detection, reduce diagnostic delays, and support overwhelmed healthcare systems, especially if it’s integrated thoughtfully into diverse clinical environments.

How do your findings provide directions for future research in the field?

They highlight the need for more large-scale, real-world testing across different demographics and healthcare environments to fully validate AI tools.

Think back to the initial curiosity that sparked your inquiry. What other curiosities do you have and how has this process prepared you to explore them?

My curiosity started with my passion for healthcare and interest in how AI can improve patient outcomes. Through this process, I’ve grown more curious about AI’s potential in diagnosing other conditions, and it’s taught me how to approach future research with confidence and structure.

How did you handle the uncertainty of the research process?

I stayed flexible, adjusted my survey outreach when responses were slow, and relied on my sources and expert input to stay on track.

If you could revisit your research process, what would you do differently and why?

I would broaden my participant pool beyond two cities to strengthen the generalizability of my results.

What was the most important research skill you developed as a result of this process, and how might you apply it to your future endeavors?

I learned how to critically evaluate and synthesize multiple sources, which will help me in future academic research and when analyzing clinical data as a future medical professional.

How did your expert adviser facilitate your deeper understanding of the research process?

My adviser helped refine my survey questions for clarity and ensured I stayed aligned with ethical guidelines, which improved both the quality and validity of my research.