Artificial intelligence in early diagnosis: integration of pre-nosological screening and personalized prevention of chronic non-communicable diseases

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Abstract

Introduction. Chronic non-communicable diseases (NCDs) account for 75% of global mortality, while traditional treatment paradigm demonstrates inability to contain epidemiological burden. Artificial intelligence (AI) technologies combined with telemedicine enable healthcare transformation: from reactive treatment to proactive health management through personalized prevention. Russian school of pre-nosological diagnostics, focused on identifying pre-pathological states through assessment of body’s functional reserves, creates methodological foundation for personalized approach that can be significantly enhanced by modern machine learning methods.

Objective: to develop methodology for remote questionnaire-based screening of NCDs using AI with integration of holistic approach to pre-nosological diagnostics, providing generation of personalized prevention recommendations, and evaluate its effectiveness in young adults.

Material and methods. Study included 3,155 university students from St. Petersburg (mean age 19.6±1.5 years) from 83 regions of Russian Federation. AI-based technology for remote screening was developed using holistic approach. System verifies risk factors by five pathology profiles (cardiology, gastroenterology, pulmonology, endocrinology, oncology). Questionnaire contains 198 information requests. Decision rules system (1,098 rules) was applied. Systematic literature review in PubMed, Scopus, Web of Science, eLibrary for 2020–2025 was conducted; RCTs, systematic reviews, WHO and Food and Drug Administration regulatory documents, methodological guidelines were analyzed.

Results. Low NCD risk detected in 57.4%, moderate in 30.9%, high in 11.7% of examined individuals. Most frequent complaints related to endocrine (28.9%), digestive (21.8%), respiratory (21.1%), and cardiovascular systems (20.1%). More than 75% showed signs of polymorbidity. Statistical analysis confirmed significant consistency between system and physician assessments (p < 0.001). Cohen’s kappa showed substantial agreement for cardiology and pulmonology profiles, moderate for gastroenterology and endocrinology. System generates personalized recommendations considering age, gender, anthropometric data, harmful habits, and psychological state. Physician time savings reached 20%. User satisfaction – 96.6%, healthcare workers – 91.7%.

Conclusion. Developed methodology for remote questionnaire-based AI screening with holistic approach showed high effectiveness for early risk factor detection in young adults. Integration of Russian pre-nosological diagnostics experience through pathology profiles with modern machine learning technologies creates conditions for transition to personalized prevention focused on correction of body’s functional reserves. System demonstrates significant social and economic effectiveness.

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About the authors

Pavel Vasilyevich Seliverstov

Federal State Budgetary Educational Institution of Higher Education “S.M. Kirov Military Medical Academy” of the Ministry of Health of the Russian Federation

Author for correspondence.
Email: seliverstovpv@yandex.ru
ORCID iD: 0000-0001-5623-4226
SPIN-code: 6166-7005

Associate Professor of the 2nd Department (advanced medical therapy), Responsible for Coordinating Scientific Work at the Department, Associate Professor

Russian Federation, Lebedeva St., 6, Saint-Petersburg, 194044

Valentin Viktorovich Shapovalov

Peter the Great Saint-Petersburg Polytechnic University

Email: valshapovalov@mail.ru
ORCID iD: 0000-0002-9764-4018
SPIN-code: 7996-2771

Professor, Institute of Biomedical Systems and Biotechnology, Doctor of Technical Sciences, Professor

Russian Federation, Saint-Petersburg, Politechnicheskaya str., 29, 194064

Yurii Alekseevich Kravchuk

Federal State Budgetary Educational Institution of Higher Education “S.M. Kirov Military Medical Academy” of the Ministry of Health of the Russian Federation

Email: kravchuk2003@mail.ru
ORCID iD: 0000-0001-8347-0531
SPIN-code: 6767-5189

Professor of the 2nd Department (Therapy for Advanced Medical Studies), Doctor of Medical Sciences, Professor

Russian Federation, Lebedeva St., 6, Saint-Petersburg, 194044

Svetlana Petrovna Salikova

Federal State Budgetary Educational Institution of Higher Education “S.M. Kirov Military Medical Academy” of the Ministry of Health of the Russian Federation

Email: salikova.1966@bk.ru
ORCID iD: 0000-0003-4839-9578
SPIN-code: 2012-8481

Professor of the 2nd Department (Therapy for Advanced Medical Studies), Doctor of Medical Sciences, Professor

Russian Federation, Lebedeva St., 6, Saint-Petersburg, 194044

Sergey Sergeevich Kupov

LLC Oncology Clinic, Scientific and Practical Center for Supportive Cancer Therapy, Integrative and Immuno-Oncology “Onco Rehab”

Email: kupov@onco.rehab.ru
ORCID iD: 0009-0009-8696-9579

Oncologist, Chief Physician, LLC Oncology Clinic, Scientific and Practical Center for Supportive Cancer Therapy, Candidate of Medical Sciences

Russian Federation, Stadionnaya St., 2, Orekhovo-Zuyevo, Moscow Region, 142603

Tatiana Nikolaevna Seliverstova

S.N. Fedorov NMRC “MNTK “Eye Microsurgery”

Email: tanyaseliverstova@yandex.ru
ORCID iD: 0009-0000-7497-3575

Ophthalmologist, Consultative and Diagnostic Department

Russian Federation, Yaroslav Hashek str., 21, Saint-Petersburg, 192283, Russian Federation

Evgeniya Andreevna Zadorozhnaya

Patrice Lumumba Peoples’ Friendship University of Russia (RUDN University)

Email: zadorojnaya2001@mail.ru
ORCID iD: 0000-0001-8916-4164

6th year Student

Russian Federation, Miklukho-Maklaya St., 6, 117198, Moscow

Nadezhda Mevludievna Mikeladze

FSAEI HE I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)

Email: imikeladze2.06@gmail.com
ORCID iD: 0009-0002-9147-7893

6th year Student

Russian Federation, Trubetskaya st., 8, build. 2, Moscow, 119991

Ksenia Valentinovna Kormschikova

FSBEI HE “Russian University of Medicine” of the Ministry of Health of the Russian Federation

Email: ksenikor@list.ru
ORCID iD: 0009-0009-1463-0023

6th year Student

Russian Federation, Dolgorukovskaya St., 4, Moscow, 127006

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