Page 29 - IMDR Journal 2025

Research Article
diabetic patients. ● Gender and Age Bias: Some AI models may perform
better for male patients than female patients, or may
Using AI to Optimize Healthcare Resources
misinterpret diabetes risks in younger individuals due to a
Decision support systems (DSS) driven by AI increase bias toward older patient data.
clinical efficiency by automating regular diabetes care
procedures and freeing up physicians to concentrate on ● Healthcare Access Disparities: Patients in rural and low-
high-risk patients. Long-term treatment expenses can be income regions may have limited access to AI-driven
decreased by using automated insulin administration healthcare solutions, worsening existing healthcare
devices instead of costly hospital-administered insulin inequalities.
therapy. Healthcare professionals can prioritize resources Addressing AI Bias for Fair Healthcare Access
for diabetes patients who are most at risk of complications
● Diverse and Representative Training Data: AI developers
with the help of AI-based population health management must train models on large, diverse datasets to ensure
systems. accuracy across different patient demographics.
AI-Powered Cost Prediction and Insurance Models ● Bias Auditing and Fairness Metrics: Healthcare AI
AI models provide individualized insurance plans by systems must undergo bias detection tests to identify and
evaluating treatment adherence and individual risk factors, rectify unintended discriminatory patterns.
which lessens patients' financial constraints. Healthcare
systems can reduce insurance fraud and avoid needless
diabetes-related invoicing by utilizing AI-powered fraud ● Equitable AI Deployment: Governments and healthcare
detection algorithms. organizations must ensure that AI-driven diabetes
management tools are accessible to underprivileged
Challenges and Ethical Concerns
communities.
Although AI has shown great promise in Type 2 Diabetes Regulatory and Legal Barriers
Mellitus (T2DM) predictive healthcare, there are a number
of obstacles and moral dilemmas associated with its Despite AI’s potential, its adoption in diabetes management
application. Concerns about patient trust, algorithmic bias, is hindered by a lack of clear regulations and legal
data privacy, and regulatory obstacles are brought up by the frameworks. AI-driven healthcare solutions must adhere to
use of AI-based models in healthcare. To guarantee fair, safe, medical, ethical, and safety standards, but regulatory
and efficient AI-driven healthcare solutions, these issues uncertainty slows widespread adoption.
need to be methodically resolved. The Absence of Standardized AI Regulations
AI-driven diabetes care systems lack universal regulatory
guidelines, making it challenging for healthcare providers to
Security and Privacy of Data implement them at scale. Countries have varying levels of
Cybersecurity Threats and Data Breach Risks AI oversight, with some regions having strict data protection
laws (e.g., Europe’s GDPR), while others have minimal AI
AI models are prime candidates for hacks because they
governance.
handle and preserve extremely private medical data.
Financial fraud, identity theft, and abuse of personal health Ethical Dilemmas in AI-Driven Medical Decision-
information (PHI) can result from data breaches in the Making
healthcare industry. The rise in ransom ware assaults against ● Liability Issues It is unclear who is legally liable in the
AI-powered medical databases underscores the necessity of event that an AI model recommends an inaccurate diagnosis
more robust protection and encryption measures. or course of treatment the institution, the software
Adherence to International Data Protection Regulations developer, or the healthcare professional.
Applications of AI in healthcare must adhere to stringent ● Autonomy vs. AI Intervention: AI systems provide
regulatory frameworks, including automated insulin dosing recommendations, but should
doctors always follow AI-based advice, or retain full control
i. General Data Protection Regulation (GDPR) – Europe:
Governs data protection and privacy, ensuring that AI over treatment decisions?
models respect patient consent and confidentiality. ● Patient Consent in AI-Based Care Some patients may not
completely comprehend AI decision-making processes,
ii. Health Insurance Portability and Accountability Act raising questions about informed consent and openness.
(HIPAA) – USA: Regulates the storage, access, and sharing
of healthcare data to prevent misuse. The Need for International AI Health Regulations
iii.Personal Data Protection Bill – India: Establishes Clear criteria for the use of AI in predictive healthcare must
guidelines for secure handling of medical data. be established by regulatory agencies like the FDA (USA),
EMA (Europe), and WHO. Before AI-driven healthcare
Algorithmic Bias and Fairness
solutions to be extensively used to manage diabetes, they
Causes of Algorithmic Bias must first pass stringent clinical testing and validation.
● Underrepresentation of Minority Groups: AI models Explain ability, accountability, and safety in AI-driven
trained on limited or non-diverse datasets may fail to decision-making must all be guaranteed by ethical AI
accurately predict diabetes risks for certain ethnicities or governance.
socioeconomic groups.

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