Iranian Journal of

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Background: In current medical practice and medical research, most prophylactic, diagnostic and therapeutic procedures involve risks and burdens, ethical standards protect and promote health and rights of human subjects. It is required for researchers and as well as research providers and users of research results to be aware of these standards.
Methods: In this article, international regulations including Nuremberg code (1974), declaration of world medical association of Helsinki, CIOMS Guidelines briefly be reviewed at the first then after some questions about it and answers and, national ethical regulation will be reviewed.
Results: The Nuremberg code (1974) is the first and the Helsinki Declaration (1964) is the most important and valid international ethical standards. That 5th amendment of the latter performed at October 2000 and WHO/CIOMS Guidelines prepared (1982, 1992 and 2000) for its performance in different countries with different situations. The first national ethical regulation for medical researches in Iran is perhaps "medical research regulation" in 1992. Establishment of the "National Ethical Committee in Medical Research" (1998) and compiling the "Codes for Protecting Human Subjects involved in Medical Research" (2000) can help medical research providers for ethical decision making.

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Diabetes accounts as one of the most prevalent diseases worldwide that affects roughly 2 million people with a prevalence of 4.67% in Iran. Despite clinical knowledge of the benefits of optimal glycemic control and preventive care, current studies show that many people with diabetes don't receive such care. Changing physician behavior is a crucial step in implant of scientific evidence into improved patient outcomes. Clinical practice guidelines (CPGs) have emerged as a means for this and for reducing variation in care.
"Iranian guidelines for diabetes mellitus care" is intended for use by health care practitioners involved in the care of people with diabetes in the community, such as general physicians endocrinoligists, podiatrists, nutritionists and optometrists.
Iranian Diabetes Guidelines (IDGs) was designed for the Iranian physicians who manage diabetic patients. IDGs consist management of types of diabetes and their acute and chronic complications in primary, secondary and tertiary care levels, also screening and life style modifications.
IDGs are the first practical guideline for management of diabetic patients in Iran.

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Background: Diabetic foot is one of the main complications of diabetes, which is caused by a significant proportion of patients with diabetes and which imposes costs on the family and the health system around the world. This review aims to summarize the latest findings in the field of diabetic foot based on clinical guidelines.
Methods: In this review study, in order to achieve the best clinical guide in the field of diabetic foot by determining the search strategy and inclusion criteria, all related clinical guides were searched in the databases and websites of clinical guidelines.  Clinical guidelines related to diabetic foot care were searched and retrieved from January 2000 to early 2019 and screened separately by two researchers to select the selected clinical guideline.
Results: Based on the study criteria, the clinical guide of the International Diabetic Foot Working Group was selected as the most complete guide in the field of diabetic foot. This summary is based on the International Working Group on the Diabetic Foot (IWGDF) Guidance 2015, consist of five documents including prevention of foot ulcers in at‐risk patients, footwear and offloading principles, diagnosis, prognosis and management of peripheral artery disease, foot infections in patients with diabetes and enhance healing of chronic ulcers of the foot in diabetes.
Conclusion: This Summary Guidance for Daily Practice is intended to overview of the main aspects of the prevention and treatment of foot problems in patients with diabetes. This summary guidance is adviced at healthcare providers and specialists in foot care throughout the world.

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Background: The prevalence of overweight, obesity and related complications is increasing rapidly in the world. Also, treating this disease in the presence or absence of co-morbidities has become a challenge. In this article, based on the clinical recommendations of the American Endocrinology Association and the American College of Endocrinology, a comprehensive clinical guide has been written for the stages of treating obese patients and its individualization, and it has been tried to be adjusted as much as possible based on the conditions in Iran.
Methods: with a specific search strategy, a complete search was performed in PubMed, Scopus, ISI Web of Science, EMBASE and Google Scholar Cochrane databases. Then, the best clinical guidelines suitable for the Iranian society were selected and using the opinions of specialists and clinical experts, a clinical guideline was prepared for the treatment of obesity in Iranian adults.
Results: In this article, in continuation of the previous article, we answered the questions number 4 to 6 regarding the stages of obesity treatment and its individualization in adults of Iranian society, and presented a total of 60 recommendations in this regard.
Conclusion: In this part of the clinical guide for obesity in Iranian adults, we tried to have a special view on the treatment of these patients and by providing evidence-based recommendations and statements, the treatment process was personalized as much as possible for patients with special conditions so that decision-making in this regard is facilitated for the relevant colleagues in this field.

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Diabetes mellitus (DM) is a chronic and multi-organ disease that affects a large population worldwide, and its incidence is increasing despite the preventive measures taken globally. Despite effective guidelines for managing DM, healthcare professionals and patients/caregivers often struggle to understand and follow these recommendations. Therefore, this study presents a comprehensive approach to diabetes management based on the American Diabetes Association's latest guidelines, aiming to facilitate the process of adherence to these guidelines within the Iranian population. The study's focus encompassed recommendations pertaining to patient communication, structured patient education and support, psychosocial factors, social determinants of health, avoidance of therapeutic inertia, individualized patient pathophysiology, continuous and effective management and care organization, mitigation of hypoglycemia risk, continuous monitoring and surveillance, modification of health-affecting behaviors, screening, ongoing management and care for diabetes-related complications, shared decision-making, and the components of the disease care. The most recent updates to the American Diabetes Association guidelines for diabetes management were also briefly explained.

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Background: Diabetes is a serious global health problem, and effective methods for its prediction and management are essential. Conventional diagnostic approaches typically rely on tests such as oral glucose tolerance test (OGTT), fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c). Machine learning has the potential to enhance diagnostic accuracy; however, its performance and alignment with clinical guidelines require thorough evaluation.
Methods: This narrative review examines the effectiveness of machine learning in the early diagnosis of diabetes. Articles were selected based on predefined criteria and analyzed in terms of algorithm classification, output measures, involvement of clinical experts, and interpretability. Evaluation metrics such as accuracy, area under the curve (AUC), specificity and sensitivity were used to assess algorithmic performance. Relevant studies comparing prediabetes diagnosis using artificial intelligence and conventional methods were reviewed, and clinical guidelines from both domains were extracted and compared.
Results: Analysis of 41 articles showed that ANN, LR, and DNN were the most frequently used algorithms. Only 2% of the studies incorporated clinical rules and physician involvement, and 12% demonstrated model interpretability. While conventional methods rely on HbA1c and FPG tests, no clinical guidelines currently exist for AI-based diagnosis. Machine learning algorithms outperformed traditional methods, showing 29% higher sensitivity and 23% higher specificity.
Conclusion: Although artificial intelligence demonstrates superior performance in prediabetes diagnosis, limitations such as lack of interpretability and the absence of standardized clinical guidelines hinder its current clinical application. Addressing these challenges could enable AI to become a more efficient and reliable diagnostic tool.