ArticleJournal of multidisciplinary healthcare2025
Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Personalised Approach to the Management of Older People with Type 2 Diabetes Mellitus-A Comprehensive Narrative Review.Journal of personalized medicine · 2026Review
- The Diabetic Hand as a Diagnostic Blind Spot: A Case of Severe Pseudohyperglycemia Masking Critical Hypoglycemia.Cureus · 2026Article
- Comprehensive Geriatric Assessment and Geriatric Interdisciplinary Team Care Model: An Integrated Model for Older Adults.Journal of multidisciplinary healthcare · 2026Review
- Artificial intelligence in clinical microbiology: results from the first National survey by the Italian association of clinical microbiologists.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.iScience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.