Aspects of the use of artificial intelligence for disease screening and diagnosis: a narrative review
https://doi.org/10.25881/18110193_2026_1_22
Abstract
Background. The active implementation of artificial intelligence (AI) in healthcare significantly improves the effectiveness of early diagnosis of various diseases. Promising areas for AI implementation include diagnostic methods based on voice analysis, remote blood flow monitoring using photoplethysmography, eye movement tracking, and the use of smart devices for continuous health monitoring. A key objective is the development of integrated systems for comprehensive medical screening.
Aim. To evaluate the literature and analyze the results of artificial intelligence implementation for the early detection of diseases.
Materials and methods. A search was conducted in the databases PubMed (Medline), Google Scholar, eLibrary, Web of Science, Scopus, CyberLeninka for English- and Russian-language publications. We used the keywords "screening," "diagnostics," "artificial intelligence," "machine learning," "disease," "screening," "diagnostics," "artificial intelligence," "disease," "machine learning," and "deep learning." Studies from 2015 to 2025 were included based on independent review by three researchers who reached a consensus.
Results. Thirty-one papers from 1,141 publications that met the search criteria were included for this review.
Conclusions. AI-based disease screening and diagnosis provides valuable information about a patient's condition, reducing the risk of human error and missing early signs of disease.
About the Authors
A. A. Garanin
Federal State Budgetary Educational Institution of Higher Medical Education "SamSMU" of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
PhD
Samara
O. A. Rubanenko
Federal State Budgetary Educational Institution of Higher Medical Education "SamSMU" of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
DSc
Samara
Yu. A. Trusov
Federal State Budgetary Educational Institution of Higher Medical Education "SamSMU" of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
Samara
D. V. Senyushkin
Federal State Budgetary Educational Institution of Higher Medical Education "SamSMU" of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
Samara
A. V. Kolsanov
Federal State Budgetary Educational Institution of Higher Medical Education "SamSMU" of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
DSc, Professor, Corresponding Member of the Russian Academy of Sciences
Samara
References
1. Morozov SP, Vladzimirskiy АV, Gombolevskiy VА, Klyashtorny VG, Fedulova IА, Vlasenkov LА. Artificial intelligence in lung cancer screening: assessment of the diagnostic accuracy of the algorithm analyzing low-dose computed tomography. Tuberculosis and Lung Diseases. 2020; 98(8): 24-31. (In Russ.) doi: 10.21292/2075-1230-2020-98-8-24-31.
2. Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med. 2022; 60(12): 1974-1983. doi: 10.1515/cclm-2022-0291.
3. Kachaeva GI, Mustafayev AG. The use of neural networks for the automatic analysis of electrocardiograms in diagnosis of cardiovascular diseases. Herald of Daghestan State Technical University. Technical Sciences. 2018; 45(2): 114-124. (In Russ.) doi: 10.21822/2073-6185-2018-45-2-114-124
4. Borodulina EA, Gogoberidze YT, Prosvirkin IA, et al. Effectiveness of artificial intelligence for lung disease screening in a municipal hospital. Siberian Journal of Clinical and Experimental Medicine. 2025; 40(1): 209-217. (In Russ.) doi: 10.29001/2073-8552-2025-40-1-209-217
5. Segar MW, Vaduganathan M, Patel KV, et al. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score Diabetes Care 2019; 42: 2298-2306. doi: 10.2337/dc19-0587.
6. Servis podderzhki prinyatiya vrachebnyh reshenij dlya diagnostiki novoobrazovanij kozhi ProRodinki po TU 58.29.32-001-43490523-2022. Registracionnoe udostoverenie medicinskogo izdeliya. № RZN 2023/21776, Prikaz ot 27.12.23 №9743. (In Russ.)
7. Freeman K, Dinnes J, Chuchu N, et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ. 2020; 368: m127. doi: 10.1136/bmj.m127.
8. Vladimirova YUV, Tyurikov MI, Loskutov IA, Katalevskaya EA. Programma skrininga diabeticheskoj retinopatii na osnove algoritmov iskusstvennogo intellekta. Effektivnaya farmakoterapiya. 2024; 20(15): 60-63. (In Russ.) doi: 10.33978/2307-3586-2024-20-15-60-63.
9. Ezra Receives 510(k) FDA Clearance for AI that enhances MRI, enabling fast, low-cost scans.
10. Perelomova PA. Automatic recognition of pathologies on X-ray, CT and MRI images. International scientific journal Bulletin of Science. 2025; 1(3)(84): 520-525.
11. Varma N, Cygankiewicz I, Turakhia M. 2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals. Russian Journal of Cardiology. 2021; 26(1S): 4420.
12. Ilhan B, Lin K, Guneri P, Wilder-Smith P. Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence. J Dent Res. 2020; 99(3): 241-248. doi: 10.1177/0022034520902128.
13. Pitchika V, Büttner M, Schwendicke F. Artificial intelligence and personalized diagnostics in periodontology: A narrative review. Periodontol 2000. 2024; 95(1): 220-231. doi: 10.1111/prd.12586.
14. Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021; 115: 103849. doi: 10.1016/j.jdent.2021.103849.
15. Shmatko A, Ghaffari Laleh N, Gerstung M, Kather JN. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat Cancer. 2022; 3(9): 1026-1038. doi: 10.1038/s43018-022-00436-4.
16. Koski E, Murphy J. AI in Healthcare. Stud Health Technol Inform. 2021; 284: 295-299. doi: 10.3233/ SHTI210726. PMID: 34920529.
17. Li JO, Liu H, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res. 2021; 82: 100900. doi: 10.1016/j.preteyeres.2020.100900.
18. Sung J. Artificial intelligence in medicine: Ethical, social and legal perspectives. Ann Acad Med Singap. 2023; 52(12): 695-699. doi: 10.47102/annals-acadmedsg.2023103.
19. Lorkowski J, Kolaszyńska O, Pokorski M. Artificial Intelligence and Precision Medicine: A Perspective. Adv Exp Med Biol. 2022; 1375: 1-11. doi: 10.1007/5584_2021_652.
20. Wang D, Li J, Sun Y, et al. A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Front Public Health. 2021; 9: 754348. doi: 10.3389/fpubh.2021.754348.
21. Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019; 21(1): 74-85. doi: 10.1002/ejhf.1333.
22. Wijnberge M, Geerts BF, Hol L, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020; 323(11): 1052- 1060. doi: 10.1001/jama.2020.0592.
23. Januzzi JLJ, Sattar N, Vaduganathan M, Magaret CA, et al. A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease. Cardiovasc Diabetol. 2025; 24(1): 213. doi: 10.1186/s12933-025-02779-5.
24. Kann BH, Likitlersuang J, Bontempi D, et al. Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial. Lancet Digit Health. 2023; 5(6): e360-e369. doi: 10.1016/S2589-7500(23)00046-8.
25. Ginder C, Li J, Halperin JL, et al. Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring. J Am Coll Cardiol. 2023; 81(10): 949-961. doi: 10.1016/j.jacc.2022.12.024.
26. Yu W, Lu Y, Shou H, et al. A 5-year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms. Cancer Med. 2023; 12(6): 6867-6876. doi: 10.1002/cam4.5477.
27. Bertoncelli CM, Latalski M, Bertoncelli D, et al. Prediction Model for Identifying Computational Phenotypes of Children with Cerebral Palsy Needing Neurotoxin Treatments. Toxins (Basel). 2022; 15(1): 20. doi: 10.3390/toxins15010020.
28. Su Z, Huang F, Yin C, Yu Y, Yu C. Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction. J Orthop Surg (Hong Kong). 2023; 31(2): 10225536231177102. doi: 10.1177/10225536231177102.
29. Wang H, Liu Y, Li Y. Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening. J Healthc Eng. 2022; 2022: 2779686. doi: 10.1155/2022/2779686.
30. Yu Y, He Z, Ouyang J, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine. 2021; 69: 103460. doi: 10.1016/j.ebiom.2021.103460.
31. Chen Y, Tian X, Fan K, Zheng Y, Tian N, Fan K. The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study. Comput Math Methods Med. 2022; 2022: 2864170. doi: 10.1155/2022/2864170. Retraction in: Comput Math Methods Med. 2023 Oct 4; 2023: 9823173. doi: 10.1155/2023/9823173.
32. Hill NR, Groves L, Dickerson C, et al. Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England. J Med Econ. 2022; 25(1): 974-983. doi: 10.1080/13696998.2022.2102355.
33. Ohno Y, Aoyagi K, Arakita K, Doi Y, et al. Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect. Jpn J Radiol. 2022; 40(8): 800-813. doi: 10.1007/s11604-022-01270-5.
34. George E, Flagg E, Chang K, et al. Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma. AJNR Am J Neuroradiol. 2022; 43(5): 675-681. doi: 10.3174/ajnr.A7488.
35. Kobayashi M, Huttin O, Magnusson M, et al. STANISLAS Study Investigators. Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals. JACC Cardiovasc Imaging. 2022; 15(2): 193-208. doi: 10.1016/j.jcmg.2021.07.004.
36. Richette P, Vis M, Ohrndorf S, et al. Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA. RMD Open. 2023; 9(1): e002934. doi: 10.1136/rmdopen-2022-002934.
37. Wang X, Li J, Sun J, et al. A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas. Biomed Res Int. 2022; 2022: 8955227. doi: 10.1155/2022/8955227.
38. Harper JR, Cherukuri V, O'Reilly T, Yu M, et al. Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus. Neuroimage Clin. 2021; 32: 102896. doi: 10.1016/j.nicl.2021.102896.
39. Lemieux ME, Reveles XT, Rebeles J, et al. Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning. Respir Res. 2023; 24(1): 23. doi: 10.1186/s12931-023-02327-3.
40. Qin X, Xia L, Ma Q, Cheng D, Zhang C. Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically. Ren Fail. 2023; 45(2): 2271104. doi: 10.1080/0886022X.2023.2271104.
41. Papp L, Spielvogel CP, Grubmüller B, et al. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga] Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging. 2021; 48(6): 1795-1805. doi: 10.1007/s00259-020-05140-y.
42. Bhattarai S, Klimov S, Aleskandarany MA, et al. Machine learning-based prediction of breast cancer growth rate in vivo. Br J Cancer. 2019; 121(6): 497-504. doi: 10.1038/s41416-019-0539-x.
43. Fang S, Liu Z, Qiu Q, et al. Diagnosing and grading gastric atrophy and intestinal metaplasia using semisupervised deep learning on pathological images: development and validation study. Gastric Cancer. 2024; 27(2): 343-354. doi: 10.1007/s10120-023-01451-9.
44. van Beek EJR, Ahn JS, Kim MJ, Murchison JT. Validation study of machine-learning chest radiograph software in primary and emergency medicine. Clin Radiol. 2023; 78(1): 1-7. doi: 10.1016/j.crad.2022.08.129.
45. Lu Q. et al. Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine. Journal of Traditional Chinese Medical Sciences. 2023; 10(3): 321-330. doi: 10.1016/j.jtcms.2023.06.003.
46. Zhongquan H, Yuqing L, Hongyan C, Geng L, Zhiwei L. Definition of the molecular bases of cold and hot properties of traditional Chinese medicine through machine learning, Pharmacological Research - Modern Chinese Medicine. 2022; 4: 100124. doi: 10.1016/j.prmcm.2022.100124.
47. Yan L, Yangyang G, Liuqing Y, et al. Traditional Chinese Medicine Constitution and Clinical Data Association with Machine Learning for Prediction of Spontaneous Abortion, Clinical Complementary Medicine and Pharmacology. 2022; 2(2): 100016. doi: 10.1016/j.ccmp.2021.100016.
48. Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer. 2024; 130(12): 2101-2107. doi: 10.1002/cncr.35307.
49. Abdullah YI, Schuman JS, Shabsigh R, Caplan A, Al-Aswad LA. Ethics of Artificial Intelligence in Medicine and Ophthalmology. Asia Pac J Ophthalmol (Phila). 2021; 10(3): 289-298. doi: 10.1097/APO.0000000000000397.
50. Pantanowitz L, Hanna M, Pantanowitz J, et al. Regulatory Aspects of Artificial Intelligence and Machine Learning. Mod Pathol. 2024; 37(12): 100609. doi: 10.1016/j.modpat.2024.100609.
Review
For citations:
Garanin A.A., Rubanenko O.A., Trusov Yu.A., Senyushkin D.V., Kolsanov A.V. Aspects of the use of artificial intelligence for disease screening and diagnosis: a narrative review. Medical Doctor and Information Technologies. 2026;(1):22-37. (In Russ.) https://doi.org/10.25881/18110193_2026_1_22
Views:
390
JATS XML
Email this article 