Volume 1 Issue 1 (2025)

This study explores the integration of artificial intelligence (AI) technologies—including machine learning, natural language processing, and computer vision—into chronic disease management, with a focus on diabetes, hypertension, and cardiovascular diseases. A systematic review of 128 clinical trials and real-world studies (2022–2025) was conducted to assess AI’s efficacy in early detection, treatment optimization, and patient adherence. Results indicate that AI-driven predictive models reduce hospital readmission rates by 23–31% and improve medication adherence by 18–25% compared to conventional care. Challenges such as data privacy, algorithm bias, and clinical validation are also addressed. The findings highlight AI’s potential to transform chronic care delivery, emphasizing the need for interdisciplinary collaboration and regulatory frameworks.

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Chronic diseases (e.g., diabetes, hypertension, cardiovascular diseases) pose a global healthcare burden, with aging populations and urbanization exacerbating resource constraints. This study explores how artificial intelligence (AI) technologies—including machine learning (ML), natural language processing (NLP), and wearable sensor analytics—address unmet needs in chronic disease management. We systematically evaluate 12 clinical trials (2022–2025) across 8 countries, demonstrating that AI-driven predictive models reduce hospital readmission rates by 28–35% and improve patient adherence by 40% compared to conventional care. Ethical considerations, including data privacy and algorithmic bias, are integrated into a proposed governance framework. Findings highlight AI’s potential to enhance equitable healthcare delivery amid urbanization-related health challenges.

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Infectious diseases (e.g., COVID-19, influenza, malaria) pose recurring threats to global health, with urbanization and international travel accelerating transmission. This study explores how artificial intelligence (AI) technologies—including predictive analytics, computer vision, and natural language processing (NLP)—enhance infectious disease prevention and control. We analyze 15 real-world implementations (2022–2025) across 10 countries, showing AI-driven early warning systems reduce outbreak response time by 40–50% and optimize resource allocation, cutting vaccine waste by 30%. Ethical challenges, such as data sovereignty and equitable access to AI tools, are addressed through a proposed global collaboration framework. Findings highlight AI’s critical role in building resilient health systems amid evolving infectious disease risks.

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The global aging population poses unprecedented challenges to healthcare systems, with 1 in 6 people worldwide expected to be over 65 by 2050 (UN, 2024). Geriatric health management—focused on chronic disease (e.g., osteoporosis, chronic obstructive pulmonary disease [COPD]) and age-related conditions (e.g., mild cognitive impairment [MCI])—requires personalized, continuous care that conventional models struggle to provide. This study explores how artificial intelligence (AI) technologies, including wearable sensor analytics, computer vision, and natural language processing (NLP), address these gaps. We analyze 18 real-world AI implementations (2022–2025) across 12 countries, showing AI-driven chronic disease monitoring reduces emergency hospital visits by 32–40%, cognitive impairment screening cuts diagnostic time by 50%, and remote care platforms improve patient satisfaction by 45%. Ethical considerations, such as digital literacy disparities and data privacy for elderly populations, are addressed through a tailored governance framework. Findings highlight AI’s potential to enhance quality of life and reduce healthcare costs for aging populations.

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Rare diseases affect 300 million people globally, with 50% occurring in children—yet 70% of pediatric rare disease patients face a diagnostic delay of 3–5 years, and 30% never receive a definitive diagnosis (Orphanet, 2024). This diagnostic gap stems from three key challenges: (1) nonspecific early symptoms (e.g., developmental delay, fatigue) that overlap with common childhood conditions, (2) limited access to genetic testing and specialist care (especially in low- and middle-income countries [LMICs]), and (3) the sheer diversity of rare diseases (over 7,000 identified to date). Artificial intelligence (AI) technologies—including machine learning (ML) for symptom pattern recognition, natural language processing (NLP) for electronic health record (EHR) analysis, and deep learning for genomic sequencing—are transforming pediatric rare disease care. This study analyzes 22 AI implementations (2022–2025) across 15 countries, showing AI reduces diagnostic time by 60–70%, increases genetic testing accuracy by 45%, and improves personalized treatment response rates by 35%. Barriers to adoption, including data scarcity (75% of rare diseases have <1,000 documented cases) and specialist distrust of AI outputs, are addressed via a collaborative governance framework. Findings highlight AI’s potential to mitigate health disparities in rare disease care, particularly for underserved pediatric populations in LMICs.

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