Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients

Abstract

The global aging population is expanding at an unprecedented rate and is projected to reach 2 billion by 2050, presenting significant medical challenges, particularly multimorbidity and heterogeneous responses to treatment. Using diabetes as an illustrative case, this study explores the transformative potential of artificial intelligence (AI)-assisted clinical decision-making to advance personalized precision medicine for older adults. Through systematic analysis of current healthcare practices and emerging AI technologies, we examined the integration of machine learning algorithms, natural language processing, and intelligent monitoring systems into diabetes care for elderly populations. Based on current evidence showing up to 25% reduction in hospitalization rates and 30% increase in treatment adherence, we argue that AI integration represents a transformative approach to improving clinical outcomes in elderly diabetes care. We contend that AI-driven clinical decision support systems (CDSS) offer superior performance in risk prediction and treatment optimization, with studies demonstrating diagnostic accuracy rates of up to 93.07%, supporting our argument for their widespread implementation. Furthermore, AI-enhanced monitoring systems improved medication adherence by 17.9% compared to conventional monitoring approaches. Nonetheless, several challenges persist, including issues related to data standardization, algorithm transparency, and patient privacy protection. These results underscore the necessity of adopting a balanced implementation strategy that addresses both technical limitations and ethical considerations, while upholding patient autonomy. This perspective emphasizes the critical importance of multidisciplinary collaboration among healthcare professionals, technology developers, and regulatory authorities in establishing a comprehensive framework for AI deployment in clinical settings. By demonstrating the capacity of AI-assisted clinical decision-making to enhance healthcare quality and efficiency for elderly patients with diabetes, this study makes a meaningful contribution to the evolving field of personalized medicine.

Keywords: aged; clinical; decision support systems; diabetes mellitus; machine learning; medical informatics; telemedicine; type 2.