Page 25 - IMDR Journal 2025

Research Article
glucose monitors, wearable technology, and smartphone medical decision-making, algorithm transparency, and AI
apps with AI capabilities can give patients immediate bias.
feedback and assist them in managing their own health.
5. To offer suggestions for improving AI-powered
Resource Limitations in Healthcare Systems: healthcare tactics.
Due to a lack of resources and the high patient loads they ● Describe how AI-powered diabetes management systems
frequently handle, patient monitoring may be compromised. will develop in the future.
By evaluating patient data, detecting risk indicators, and ● Suggestions on how to use AI technologies into traditional
recommending prompt interventions, AI-powered decision healthcare while making sure that ethical issues are taken
support systems can help physicians.
into account.

OBJECTIVES : LITERATURE REVIEW
With an emphasis on their effects on patient outcomes,
In healthcare, artificial intelligence (AI) has become a game-
glycemic control, and healthcare expenses, this study changer, improving patient management, disease
attempts to investigate the function of AI-driven predictive identification, and therapy optimization. The application of
healthcare models in the proactive management of Type 2 AI-driven solutions to diabetes care has demonstrated great
diabetes. This study aims to provide light on how AI can promise in early diagnosis, predictive analytics, and
improve disease monitoring, lower complications, and individualized treatment regimens as these technologies
boost overall treatment effectiveness by evaluating the continue to advance. This section examines the body of
efficacy of AI-based solutions in diabetes management. research on artificial intelligence (AI) applications in
To achieve the above aim, this study is structured around the healthcare, with a focus on Type 2 Diabetes Mellitus
following key objectives: (T2DM) prediction, management, and treatment.
Healthcare AI Applications
1. To investigate AI's use in predictive analytics for the AI has completely changed how diseases are identified,
treatment of Type 2 diabetes. tracked, and treated, among other aspects of healthcare. The
following are a few significant uses of AI in the medical
● Examine how AI-powered algorithms use patient data to
forecast the onset and course of diabetes. field:
● Examine how big data analytics, deep learning networks, Smarter Medical Diagnoses with AI
and machine learning algorithms contribute to risk Artificial Intelligence is changing how we detect diseases.
assessment and early detection. Using machine learning and deep learning, doctors can now
catch illnesses like diabetes, cancer, and heart problems
much earlier than before.
2. To evaluate how well predictive healthcare models
powered by AI can lower problems. ● Scans & Imaging: AI can look at CT scans, MRIs, and X-
rays to spot things that might be missed by the human eye.
● Analyse how AI affects glucose management and whether This makes diagnosis more accurate and helps reduce
it can avert serious side effects such diabetes retinopathy, mistakes.
neuropathy, and heart conditions.
● Lab Tests & Pathology: With AI, blood tests and lab
● Examine how wearable technology, insulin pumps, and reports can be read faster and more precisely. It helps doctors
continuous glucose monitors (CGMs) with AI capabilities find signs of disease — like specific markers — much more
enhance diabetes self-management.
efficiently.
AI-Enabled Robotic Surgeries
3. To investigate how AI affects resource optimization and AI is also stepping into the operating room. Robotic systems
healthcare expenses.
— like the well-known da Vinci Surgical System — are
● Examine whether using AI to treat diabetes results in helping surgeons perform complex surgeries with tiny cuts,
fewer hospital stays, quicker consultation times, and better leading to quicker healing and fewer risks. These robots,
use of available healthcare resources. powered by AI, help guide the surgeon using real-time
● Examine how medical practitioners might optimize images and patterns. This means better precision and
diabetes treatment programs with the aid of AI-powered smarter decisions during surgery.
decision support technologies. Faster Drug Discovery with AI
Creating new medicines takes time — often years. AI is
4. To determine the obstacles and moral dilemmas helping cut that down. By studying how chemicals might
associated with the deployment of AI. work in the body, AI can suggest which ones could become
effective treatments, including for long-term illnesses like
● Examine the security and privacy concerns related to AI- diabetes. Deep learning tools can even predict how a drug
powered medical applications.
might behave before it’s tested on people — saving both
● Analyse concerns about patient trust in AI-assisted time and cost in research.

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