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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

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Dana Suyunbay

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Review Open access Jul 2026

ARTIFICIAL INTELLIGENCE IN ENDOCRINOLOGY: EMERGING APPLICATIONS IN DIABETES AND OBESITY MANAGEMENT

Artificial intelligence has rapidly emerged as one of the most transformative technologies in modern endocrinology, particularly in the management of diabetes mellitus and obesity. The integration of machine learning, deep learning, natural language processing, and generative AI into endocrine practice has enabled more accurate disease prediction, individualized treatment strategies, automated insulin delivery, and continuous patient monitoring. AI-powered clinical decision support systems facilitate early diagnosis of metabolic disorders, optimize glucose control through continuous glucose monitoring, predict diabetes-related complications, and improve obesity risk stratification. Furthermore, wearable technologies combined with AI algorithms provide real-time analysis of physiological parameters, enabling personalized interventions and improving long-term clinical outcomes. Recent developments in explainable AI, digital twins, and large language models have expanded opportunities for precision endocrinology while simultaneously introducing new ethical, regulatory, and cybersecurity challenges. This review summarizes current evidence regarding the emerging applications of AI in diabetes and obesity management, discusses recent technological advances, highlights implementation barriers, and explores future directions for AI-assisted endocrine care.

M. Abdumalikova, Ulykbek Daurenov, Dana Suyunbay et al. · 0 citations
Review Jul 2026

CURRENT ADVANCES IN CLINICAL ONCOLOGY: CHALLENGES AND FUTURE PERSPECTIVES

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in prevention, diagnosis, and treatment. The rapid development of molecular biology, precision medicine, immunotherapy, artificial intelligence, and genomic technologies has transformed the landscape of modern oncology, enabling more individualized and effective patient care. Recent breakthroughs in liquid biopsy, next-generation sequencing, targeted therapies, immune checkpoint inhibitors, and AI-assisted clinical decision-making have significantly improved diagnostic accuracy, therapeutic efficacy, and survival outcomes across multiple cancer types. However, numerous challenges persist, including tumor heterogeneity, treatment resistance, immune-related adverse events, limited accessibility to innovative therapies, high healthcare costs, and disparities in cancer care across different regions. Furthermore, integrating multi-omics technologies, digital pathology, and real-world evidence into routine clinical practice requires standardized protocols and robust clinical validation. This review summarizes the most significant advances in contemporary clinical oncology, discusses current limitations affecting cancer management, and highlights emerging technologies that are expected to shape the future of personalized cancer treatment. The continued integration of genomics, AI-driven diagnostics, biomarker-guided therapies, and multidisciplinary clinical approaches is anticipated to improve patient outcomes while promoting precision oncology as the new standard of cancer care.

Ulykbek Daurenov, Dastan Turdykul, Dana Suyunbay et al. · 0 citations

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