Dicky L. Tahapary Indonesia

Dr. Dicky Tahapary is a consultant endocrinologist and Associate Professor at the Faculty of Medicine, Universitas Indonesia–Dr. Cipto Mangunkusumo National General Hospital. He leads the Metabolic-Cardiovascular-Aging Cluster at the Indonesian Medical Education and Research Institute (IMERI) and coordinates Indonesia’s national initiatives on precision medicine for diabetes and metabolic diseases. His work bridges clinical endocrinology, population-based research, and digital health innovation. His current focus includes implementing AI-driven solutions and genomic platforms to personalize diabetes management in Indonesia.

21 MARCH

Time	Session

10:20 11:10	Oral Presentation 4: Metabolic Synergy: Diabetes & Obesity Management Chee Keong SeeMalaysia Moderator AI and Digital Diabetes Care: Awareness, Utilisation and Perspectives from MalaysiaMalaysia’s substantial burden of Type 2 Diabetes (T2D) necessitates a transition from a conventional “standard-of-care” approach toward a more individualized “precision-of-care” model. In this context, Artificial Intelligence (AI)–driven Clinical Decision Support Systems (CDSS) are emerging as promising tools to address persistent clinical and treatment inertia. AI in Insulin Titration Recent evidence evaluating a real-time AI-assisted insulin titration system for glycaemic control in patients with T2D demonstrates considerable clinical potential. By integrating Continuous Glucose Monitoring (CGM) data with historical insulin dosing patterns, the system generates individualized titration recommendations. Clinical findings indicate non-inferiority to senior endocrinologists in insulin dose adjustment. Notably, use of the system was associated with improvements in Time in Range (TIR), enhanced day-to-day glycaemic stability, and a significant reduction in nocturnal hypoglycaemia. Specialist Perspective Malaysian endocrinologists have expressed cautious optimism regarding the integration of AI into diabetes management. Rather than functioning as a replacement for clinician expertise or doctor–patient interaction, AI-based systems are regarded as adjunctive decision-support tools. These systems may alleviate cognitive and administrative burden, thereby allowing clinicians to allocate greater attention to complex case management, comorbidity optimization, and individualized patient counselling. Adoption Drivers and Barriers The successful implementation of AI-driven CDSS is influenced by both technical and socio-behavioral factors. Adoption is strongly associated with performance expectancy, particularly perceived improvements in efficiency and clinical productivity, as well as positive peer influence within the medical community. Conversely, concerns surrounding data privacy, workflow disruption, system interoperability, and digital literacy—especially among older patient populations—remain significant barriers to widespread integration. Future Directions To translate technological innovation into sustainable clinical impact, strategic priorities should include seamless integration with existing Electronic Medical Record (EMR) systems, robust data governance and cybersecurity frameworks, and structured AI competency training for healthcare professionals. Ultimately, AI should be conceptualized not as a substitute for clinical judgment, but as a complementary enabler of scalable, precise, and patient-centered diabetes care in Malaysia. Dicky L. TahaparyIndonesia Moderator Precision Diabetes Clustering for Young-Onset Diabetes (in Indonesia Populations) 201BC

Time

Session

10:20

11:10

Oral Presentation 4: Metabolic Synergy: Diabetes & Obesity Management

Chee Keong SeeMalaysia Moderator AI and Digital Diabetes Care: Awareness, Utilisation and Perspectives from MalaysiaMalaysia’s substantial burden of Type 2 Diabetes (T2D) necessitates a transition from a conventional “standard-of-care” approach toward a more individualized “precision-of-care” model. In this context, Artificial Intelligence (AI)–driven Clinical Decision Support Systems (CDSS) are emerging as promising tools to address persistent clinical and treatment inertia. AI in Insulin Titration Recent evidence evaluating a real-time AI-assisted insulin titration system for glycaemic control in patients with T2D demonstrates considerable clinical potential. By integrating Continuous Glucose Monitoring (CGM) data with historical insulin dosing patterns, the system generates individualized titration recommendations. Clinical findings indicate non-inferiority to senior endocrinologists in insulin dose adjustment. Notably, use of the system was associated with improvements in Time in Range (TIR), enhanced day-to-day glycaemic stability, and a significant reduction in nocturnal hypoglycaemia. Specialist Perspective Malaysian endocrinologists have expressed cautious optimism regarding the integration of AI into diabetes management. Rather than functioning as a replacement for clinician expertise or doctor–patient interaction, AI-based systems are regarded as adjunctive decision-support tools. These systems may alleviate cognitive and administrative burden, thereby allowing clinicians to allocate greater attention to complex case management, comorbidity optimization, and individualized patient counselling. Adoption Drivers and Barriers The successful implementation of AI-driven CDSS is influenced by both technical and socio-behavioral factors. Adoption is strongly associated with performance expectancy, particularly perceived improvements in efficiency and clinical productivity, as well as positive peer influence within the medical community. Conversely, concerns surrounding data privacy, workflow disruption, system interoperability, and digital literacy—especially among older patient populations—remain significant barriers to widespread integration. Future Directions To translate technological innovation into sustainable clinical impact, strategic priorities should include seamless integration with existing Electronic Medical Record (EMR) systems, robust data governance and cybersecurity frameworks, and structured AI competency training for healthcare professionals. Ultimately, AI should be conceptualized not as a substitute for clinical judgment, but as a complementary enabler of scalable, precise, and patient-centered diabetes care in Malaysia.

Dicky L. TahaparyIndonesia Moderator Precision Diabetes Clustering for Young-Onset Diabetes (in Indonesia Populations)

201BC

22 MARCH

Time	Session

14:00 15:30	[Symposium] AI in Diabetic Management Jae Hoon MoonSouth Korea Moderator A New Era of Managing Thyroid Eye Disease: AI-Based Quantitative Monitoring and Precision CareThyroid Eye Disease (TED) is the most common extrathyroidal manifestation of autoimmune thyroid dysfunction, occurring in approximately 30% to 50% of patients with Graves’ disease. While endocrinologists primarily manage thyroid dysfunction, TED can severely impact a patient’s quality of life through vision loss, diplopia, and cosmetic concerns, necessitating active early intervention. Consequently, it is crucial for clinicians to be proficient in basic TED assessments for early diagnosis; however, many endocrinologists remain unfamiliar with these evaluations, which often leads to delayed treatment. To usher in a new era of managing TED, a paradigm shift toward AI-based quantitative monitoring and precision care is explored in this session. Fundamental assessment methods, including the Clinical Activity Score (CAS), exophthalmos, and Margin-Reflex-Distance 1 (MRD1), will be introduced alongside clinical cases where AI-driven solutions provide objective and reproducible data. These cutting-edge tools go beyond simple diagnostic assistance by quantitatively tracking disease progression and treatment response, thereby facilitating highly personalized treatment plans. By integrating these innovative AI solutions, a comprehensive approach to TED management is presented, demonstrating how technology and innovation converge to solve long-standing clinical challenges and improve patient outcomes. Yao-Hsien TsengTaiwan Moderator AI and Clinical Decision Support System (CDSS) for Type 1 Diabetes Luqman IbrahimMalaysia Speaker AI and Clinical Decision Support System (CDSS) for Type 1 Diabetes AI applications and insulin management Iris Isip-TanPhilippines Speaker AI applications and insulin managementArtificial intelligence is evolving from novel research concepts to practical clinical tools. This presentation will provide a broad overview of the current AI ecosystem, from automated insulin delivery (AID) systems to machine learning algorithms designed for glucose prediction and clinical decision support. The unique regional perspective will be addressed, exploring how diverse healthcare infrastructures and reimbursement models influence the adoption of AI innovations. The discussion will conclude by identifying key challenges in implementation, such as algorithmic equity and data security, to outline a framework for the future integration of AI into daily practice. Precision Diabetes Clustering for Young-Onset Diabetes (in Indonesia Populations) Dicky L. TahaparyIndonesia Speaker Precision Diabetes Clustering for Young-Onset Diabetes (in Indonesia Populations) 201AF

Time

Session

14:00

15:30

[Symposium] AI in Diabetic Management

Jae Hoon MoonSouth Korea Moderator A New Era of Managing Thyroid Eye Disease: AI-Based Quantitative Monitoring and Precision CareThyroid Eye Disease (TED) is the most common extrathyroidal manifestation of autoimmune thyroid dysfunction, occurring in approximately 30% to 50% of patients with Graves’ disease. While endocrinologists primarily manage thyroid dysfunction, TED can severely impact a patient’s quality of life through vision loss, diplopia, and cosmetic concerns, necessitating active early intervention. Consequently, it is crucial for clinicians to be proficient in basic TED assessments for early diagnosis; however, many endocrinologists remain unfamiliar with these evaluations, which often leads to delayed treatment. To usher in a new era of managing TED, a paradigm shift toward AI-based quantitative monitoring and precision care is explored in this session. Fundamental assessment methods, including the Clinical Activity Score (CAS), exophthalmos, and Margin-Reflex-Distance 1 (MRD1), will be introduced alongside clinical cases where AI-driven solutions provide objective and reproducible data. These cutting-edge tools go beyond simple diagnostic assistance by quantitatively tracking disease progression and treatment response, thereby facilitating highly personalized treatment plans. By integrating these innovative AI solutions, a comprehensive approach to TED management is presented, demonstrating how technology and innovation converge to solve long-standing clinical challenges and improve patient outcomes.

Yao-Hsien TsengTaiwan Moderator

AI and Clinical Decision Support System (CDSS) for Type 1 Diabetes

Luqman IbrahimMalaysia Speaker AI and Clinical Decision Support System (CDSS) for Type 1 Diabetes
AI applications and insulin management

Iris Isip-TanPhilippines Speaker AI applications and insulin managementArtificial intelligence is evolving from novel research concepts to practical clinical tools. This presentation will provide a broad overview of the current AI ecosystem, from automated insulin delivery (AID) systems to machine learning algorithms designed for glucose prediction and clinical decision support. The unique regional perspective will be addressed, exploring how diverse healthcare infrastructures and reimbursement models influence the adoption of AI innovations. The discussion will conclude by identifying key challenges in implementation, such as algorithmic equity and data security, to outline a framework for the future integration of AI into daily practice.
Precision Diabetes Clustering for Young-Onset Diabetes (in Indonesia Populations)

Dicky L. TahaparyIndonesia Speaker Precision Diabetes Clustering for Young-Onset Diabetes (in Indonesia Populations)

201AF