Welcome to SEMBAeDiagnosis!

Assoc. Prof. Emil Aliyev, MD/PhD, MSc was born in 1991 in Baku, Azerbaijan SSR. He graduated from Istanbul University Istanbul Faculty of Medicine in 2014. In 2019, he completed his specialization in Pediatrics at Hacettepe University Faculty of Medicine. In the same year, he worked as a Lecturer (non-tenured) in the Department of Pediatrics at the University of High Specialization. The difficulties patients face in accessing doctors and the lack of public awareness about physician referral in healthcare led him to pursue the use of information technologies in medicine. For this reason, in September 2019, he founded SEMBA Health Education & Informatics LLC in Ankara as a 100% foreign-owned company. He continues to serve as CEO. In 2025, he completed his subspecialty in Pediatric Rheumatology at Hacettepe University Faculty of Medicine. In 2026, he began working as an independent consultant at iMerit Technologies+EXL, a world leader in AI technologies for healthcare. He currently continues his academic career as an Associate Doctor in the Department of Pediatrics at Lokman Hekim University English-medium Faculty of Medicine. The SEMBAeDiagnosis application is the product of more than 4 years of work. During this process, Dr. Emil Aliyev developed and tested closed-loop AI models to help diagnose niche diseases in his own field of pediatric rheumatology — where diagnostic criteria are lacking and physicians rely on subjective decisions — as well as many common and rare diseases. All of these were non-profit projects that evolved into the models named Morgaf for SLE, Maverik for CNO, and Galadriel for Kawasaki disease, with pilot and national-level validations completed. In addition, he integrated his personal physician experience and mental algorithms into closed-loop AI models, training them on millions of data points without requiring online data. Thanks to all these models and the application interface, success was observed not only in the pediatric age group but across all age groups and all diseases. Today, the application processes billions of data points through closed-loop deep learning with RNN modeling and approximately 10,000+ neural networks at a high success rate. All of these processes operate in compliance with laws on the 'Processing and Storage of Personal Data' in Turkey and the European Union, ensuring that patients' personal and sensitive medical data are not shared with third parties, partners, or organizations.