Page 30 - IMDR Journal 2025

Research Article
Patient Trust and Acceptance telemedicine platforms, and electronic health records
(EHRs), interoperability between AI systems and the current
Barriers to Patient Trust in AI Healthcare
healthcare infrastructure needs to be enhanced. Establishing
● Fear of AI Replacing Doctors Many patients worry that AI public-private collaborations will speed up regulatory
will replace human healthcare providers, leading to a lack of clearances, large-scale deployments, and funding for AI
personal interaction and empathy in treatment. research.
● Concerns About AI Accuracy Patients may be reluctant to Handling Security and Ethical Issues|
rely on AI-based insulin recommendations or diabetes To guarantee patient safety, equity, and trust, ethical
management plans without physician confirmation.
integrity and data security must be given top priority when
● Lack of AI Explain ability AI models often function as integrating AI into healthcare.
“black boxes”, making it difficult for patients to understand
Putting Strict Data Protection Procedures in Place
how decisions are made.
To stop unwanted access to private medical information,
Techniques to Boost Patient Confidence and AI stronger authentication and encryption procedures should
Transparency
be used. All AI-powered healthcare solutions should be
● Human-AI Collaboration When it comes to diabetes care, required to comply with regional data protection legislation,
AI should be utilized as a decision-support tool to help such as HIPAA in the USA and GDPR in Europe. Data
physicians rather than to replace them. privacy can be improved by implementing decentralized AI
● Explainable AI (XAI) AI-driven healthcare platforms models and federated learning, which enable AI systems to
should provide clear, understandable explanations of train on patient data without sending it to central servers.
treatment suggestions. Improving Explainability and Transparency of
● Patient Education and AI Awareness Medical Algorithms
professionals need to inform patients about the advantages, To guarantee that patients and doctors comprehend how AI
drawbacks, and potential contribution of AI to better makes decisions, explainable AI (XAI) models ought to be
diabetes care. given top priority in the healthcare industry. AI models
Ethical Considerations in AI Adoption ought to offer confidence scores and justifications for risk
assessments, insulin recommendations, and diagnostic
Patients should retain autonomy over their healthcare
decisions, with AI operating as a helpful tool rather than an forecasts. To check AI models for bias, fairness, and
authoritative decision-maker. Transparency, equity, and adherence to ethical standards, independent AI ethics boards
patient safety must be given top priority in AI-driven ought to be set up.
diabetes care systems in order to foster long-term adoption Increasing Patient Trust via AI-Human Cooperation
and trust. AI ought to serve as a tool for decision-making, not a
decision-maker, so that patients and doctors maintain
authority over treatment decisions. Patients should be
RECOMMENDATIONS
informed about the advantages, drawbacks, and role of AI in
Strategies for Enhancing AI Adoption diabetes management through the development of
Healthcare practitioners, legislators, and tech developers transparent AI communication tactics. Interactive AI health
must collaborate to guarantee effective integration, training, coaches, language assistance, and user interface
and accessibility in order to optimize the advantages of AI in customization are examples of patient-centric features that
diabetes treatment. should be incorporated into AI systems.
Promoting AI Education for Medical Professionals
Many healthcare professionals lack the technical know-how CONCLUSION
necessary to properly comprehend insights produced by AI.
Artificial Intelligence (AI) is transforming how we approach
Professional training and medical education should Type 2 Diabetes care. Instead of waiting for complications to
incorporate AI literacy initiatives. Predictive analytics, arise, AI helps healthcare providers act early. This shift from
automated diabetes management tools, and AI-driven a reactive to a proactive approach means problems can be
decision support systems should all be covered in detected before they become serious. Traditional diabetes
workshops and certifications offered by hospitals and management often struggles with delayed diagnoses,
clinics. Instead of taking the place of doctors' knowledge, AI generic treatment plans, and poor follow-up, which can lead
should be positioned as a therapeutic assistance that helps to long-term health issues. But AI is changing that by
them make better decisions. offering early detection, personalized care, continuous
Improving Cooperation Between IT Firms and Medical monitoring, and decision support tools that help doctors
Providers. make better choices for each patient.
To create AI-powered diabetes control systems that meet Thanks to technologies like wearable health devices, deep
practical clinical needs, government organizations, learning systems, and smart algorithms, AI is helping
healthcare facilities, and AI firms should collaborate. To improve blood sugar control, reduce complications, and
guarantee smooth integration with wearable technology, even cut down healthcare costs. It's making care more

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