Artificial Intelligence and Diabetes

Diabetes mellitus is recognized as one of the most significant chronic metabolic disorders and has emerged as a major global health challenge over recent decades. The continuous rise in the prevalence of this disease has resulted in millions of individuals worldwide experiencing its consequences, while healthcare systems face substantial financial and operational burdens associated with its management. Current projections suggest that, if existing trends persist, the global population living with diabetes may reach approximately 700 million by 2045. Beyond the growing number of affected individuals, inadequate glycemic control is a major contributing factor to the development of severe complications, including diabetic eye disease, impaired kidney function, neurological damage, cardiovascular disorders, and diabetic foot ulcers. These complications not only substantially diminish patients’ quality of life but also impose considerable costs on healthcare infrastructures.

Given this growing burden, the integration of advanced technologies, particularly artificial intelligence (AI), has attracted increasing attention as a promising strategy for improving diabetes management. This study aims to explore the potential applications and capabilities of AI-based approaches in enhancing glycemic control among individuals with diabetes. Through a comprehensive perspective, it examines the contribution of AI technologies to early disease detection, prediction of glucose fluctuations, optimization of therapeutic interventions, and reduction of the risk of both acute and long-term diabetes-related complications.

Evidence from recent research indicates that artificial intelligence techniques, including machine learning algorithms and artificial neural networks, possess considerable potential for analyzing complex glucose-related patterns due to their ability to process large-scale medical datasets and identify relationships that may not be easily detectable through conventional approaches. These computational models can anticipate episodes of severe hyperglycemia or hypoglycemia before they occur, support automated insulin regulation in artificial pancreas systems, and generate individualized treatment and self-management recommendations based on patient-specific characteristics and clinical data.

Furthermore, numerous investigations have demonstrated that AI-enabled continuous glucose monitoring systems, intelligent mobile health applications, and digital healthcare assistants can positively influence diabetes outcomes. By facilitating more accurate disease monitoring and personalized decision-making, these technologies have been associated with improvements in key clinical indicators, including reductions in glycated hemoglobin (HbA1c) levels, increased time spent within the target glucose range, and decreased frequency of hypoglycemic and hyperglycemic events. Consequently, AI-driven solutions may enhance the effectiveness of current diabetes care strategies and contribute to more proactive disease management.

Despite these promising advantages, the widespread adoption of artificial intelligence in diabetes care remains subject to several important challenges. These include limitations related to the quality, consistency, and standardization of medical data; concerns regarding patient privacy and data security; the need for greater transparency and interpretability of AI models; and difficulties associated with integrating these technologies into existing healthcare infrastructures. Therefore, further clinical research, along with the development of comprehensive regulatory frameworks and practical implementation guidelines, is essential to ensure the safe, effective, and sustainable incorporation of artificial intelligence into diabetes management. 

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A Review of Thyroid Dysfunction due to COVID-19

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Association of the Thyroid Nodules With FNA

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Abstract

Thyroid nodules are a common finding in clinical practice.

Although ultrasonography is an accepted method for evaluating

these nodules, Fine Needle Aspiration (FNA) is the procedure of

choice for assessing the risk of malignancy. This study aims to determine

the association between sonographic features of thyroid nodules based on

Thyroid Imaging Reporting and Data System classification and the cytology results.

In this prospective cohort study, 147 patients from Tehran Medical Imaging Center

who had thyroid nodules underwent ultrasonography-guided FNA,

and their sonographic features were recorded.

The pathologic findings were also obtained according to the Bethesda system.

inally, the association between sonographic features and cytological results was

analyzed.

Eighteen (12.3%) nodules were malignant, and 129 nodules (87.7%) were benign. The

association of TIRADS categories with the risk of malignancy is as follows:

TIRADS 1 (n=0, 0%), TIRADS 2 (n=10, 16.9%), TIRADS 3 (n=6, 10.5%), TIRADS 4 (n=2, 16.7%), and TIRADS 5 (n=0, 0%).

The bloody lamellae of thyroid nodules were significantly correlated with

the risk of malignancy (P<0.05).

However, there was no statistically significant association between

the risk of malignancy and gender (P=0.47), calcification (P=0.9),

firmness (P=0.19), halo sign (P=0.95), location of nodules (P=0.35),

and nodules' echogenicity (P=0.058).

Although there are trusted classifications such as TIRADS for

categorizing thyroid nodules, there is still uncertainty in utilizing them,

especially in the management of nodules classified as TIRADS 2,

.

in which various sonographic features are shared between benign and malignant nodules.

Although, based on the ACR guideline, we do not perform FNA

with all nodules that have TIRADS 2, regardless of the size or other characteristics

of the nodule,

there is a possibility that some of these nodules will be malignant in the future,

so it is considered.

It seems that the guideline should be revised regarding TIRADS 2,

and under certain circumstances,

these types of nodules should also be sampled under FNA ultrasound guidance

with some criteria such as size or other features,

because there may be problems in the future,

contrary to what the guideline currently recommends,

that sampling TIRADS 2 is not required.

 be created and find out that some of these types of nodules are malignant 

Although currently, the ACR guideline does not recommend performing

thyroid nodule FNA with TIRADS 2

unless the patient has a positive family history of thyroid cancer,

. especially if family history is the MTC type or MEN.

Author and compiler:

Dr. Alireza Arefzadeh

endocrinology and metabolism specialist in Tehran

specialist in endocrine diseases in Tehran

the best endocrinologist in Tehran

the best endocrinologist in Tehran

the best subspecialist in Iran

the best endocrinologist in Iran

the best endocrinologist in Tehran

the best endocrinologist in Iran

the best specialist Thyroid in Tehran

FNA

thyroid ultrasound

                                                                                         <Click here>

Abstract

Dr. Arefzadeh graduated from Tehran University of Medical Sciences in the field of Internal Medicine in 2008.

He graduated from Tehran University of Medical Sciences in the field of Endocrinology and Metabolism in 2012.

He has many researchs about internal medicine and endocrinology fields.

He is clinician and researcher and he works about diabetes mellitus, GDM, international education of diabetes and other endocrinology fields.

He likes teaching students, interns, residents and fellowships.

Dr. Arefzadeh has IELTS Document and he can speak three languages: English, Danish and Persian.

Dr. Arefzadeh has educated Iranians around the world regarding on the importance of Diabetes Mellitus and other metabolic disorders and has helped to resolve problems in the Iranian community.

He is member of American Diabetes Association(ADA) and European Society of Endocrinology(ESE) and European Research Association(CORDIS).

Patients can contact me via my Email address: info@drarefzadeh.com

Stress is the most important factor to predispose to diabetes.

Many diabetic patients have had a major stress before diabetes in my eperiences.

I have visited many diabetic patients who didn't  have positive diabetic family history,however, they have developed diabetes and they had major stress before diabetes.  

Stress can increase blood sugar and it can lead to bad habits like overeating, which can make diabetes harder to manage. 

Recerntly, some papers were published about this subject.

I strongly recommended to avoid stress and anxiety.

Dr. Alireza Arefzadeh.

correspondent author:Alireza Arefzadeh

Abstract
Introduction:
News media, including content and methods for presenting it,
unmistakably contribute to the informal education of our students outside the
classroom. In addition to considering new media (television, video games, Internet,
etc.) as educative agents and keeping their influence in mind, educational institutions
incorporate these media into programs of study as content—including analysis of their
importance in society, function, and the repercussions, they may have in our lives.
Discussion:
News multimedia technologies must fulfill many basic functions:
- represent and present both real and virtual worlds.
- facilitate teaching.
- enhance student learning.
- improve IQ, residential, distance memory, and life-long learning.
- accommodate students’ learning styles.
- Digital Text Writing.
- Digital knowledge
- Connect people to teachers and Students and experts.
- Online courses registry.
-More Velocity and better quality Services.
-Immediately self Examination and scored, providing quick feedback.
With the arrival of computers, written textual language was first to be digitized
because it required the least amount of resources. The new Digital text can be
modified more easily, it can be sent from one place to another, words or strings of
characters can be searched for and found instantly, etc.
Social networking sites allow students to explore their interests on a global scale and
discuss their interests with a wider range of people. This has huge implications for
self-learning, as information and resources are much more available than they were
previously. However, for students that aren’t skilled at evaluating different content, it
can be hard for them to sort through various resources to find accurate and useful
information. Important Social networking sites are
Twitter,Wikipedia,Google,Yahoo, Facebook, and Youtube, Smartphones and
Iphone. Educational institutions will successfully wed academia and technology,
allowing students to learn in new and innovative ways. Technology has great potential
to help people learn, and businesses should capitalize on the advantages of technology
to create technologically advanced educational systems. They change to accommodate
students’ learning styles and significantly change education of the future. The Internet
allows people to search through digital knowledge and connect people to teachers and
experts. Textbooks can become interactive and can easily include updates and
corrections. Students will likely be able to get coursework from whichever university
they choose, and courses will likely be collaborative and public. These changes will
allow universities to teach anyone willing to learn, rather than only a select few in a
classroom. Technologies are already changing education, as online schools allow
students to earn degrees through only online courses Computer-Based Trainings are
self-paced activities on a computer that present content to a user. The trainings often
include assessments that can be immediately scored, providing quick feedback to the
users.
Conclusion: News Media Technologies provide revolution in education.

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Accuracy of three imaging modalities for evaluation of the posterior lens capsule in traumatic cataract

Accuracy of 3 imaging modalities for evaluation of the posterior lens capsule in traumatic cataract.