Application of digital methods and artificial intelligence capabilities for diagnostics in obstetrics and gynecology

E.R. Safiullina, E.I. Rychkova, Irina Mayorova, D.Kh. Khairutdinova, A.A. Slonskaya, A.S. Faronova, Y.A. Davydova, I.A. Mussova
In: CARDIOMETRY · 2023 · pp. 111–117 · doi:10.18137/cardiometry.2023.27.111117 · W4379525468
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This paper reviews the application of digital methods and artificial intelligence in obstetrics and gynecology, finding significant potential for accurate and efficient diagnosis of various conditions by analyzing medical data.

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The paper analyzes how digital methods and artificial intelligence can be used for diagnostics in obstetrics and gynecology, emphasizing their ability to analyze medical images and other data with high accuracy and speed. It highlights examples such as AI assisting cervical cancer diagnosis by identifying anomalies in digital images and supporting screening, and it states that AI could also recognize other gynecological conditions including endometriosis, uterine fibroids, and polyps. A key caveat noted is that AI cannot fully replace doctors’ experience and expertise. Relevance to endometriosis: the paper discusses endometriosis as one of several gynecological diseases that AI could help recognize, though its overall focus is a broad review of digital/AI diagnostic capabilities across obstetrics and gynecology.

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Abstract

The article analyzes the use of digital methods and artificial intelligence capabilities for diagnostics in the field of obstetrics and gynecology. The author notes that digital methods and artificial intelligence (AI) have a high potential for the diagnosis of gynecological diseases, since it can analyze medical images and other medical data with great accuracy and speed. For example, AI can help in the diagnosis of cervical cancer by identifying anomalies in digital images and screening tests. The use of AI can also help in the recognition of other gynecological diseases, such as endometriosis, uterine fibroids, polyps, etc. In addition, AI can help improve the efficiency and accuracy of diagnostics, as well as reduce the time required to process medical data. This can be especially important in cases where diagnosis needs to be done quickly in order to start treatment as early as possible. However, it should be noted that AI cannot completely replace the experience and expertise of doctors. Still, it can help doctors make more accurate diagnoses and develop more effective treatment strategies.
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Application of digital methods and artificial intelligence capabilities for diagnostics in obstetrics and gynecology * Corresponding author Abstract The article analyzes the use of digital methods and artificial intelligence capabilities for diagnostics in the field of obstetrics and gynecology. The author notes that digital methods and artificial intelligence (AI) have a high potential for the diagnosis of gynecological diseases, since it can analyze medical images and other medical data with great accuracy and speed. For example, AI can help in the diagnosis of cervical cancer by identifying anomalies in digital images and screening tests. The use of AI can also help in the recognition of other gynecological diseases, such as endometriosis, uterine fibroids, polyps, etc. In addition, AI can help improve the efficiency and accuracy of diagnostics, as well as reduce the time required to process medical data. This can be especially important in cases where diagnosis needs to be done quickly in order to start treatment as early as possible. However, it should be noted that AI cannot completely replace the experience and expertise of doctors. Still, it can help doctors make more accurate diagnoses and develop more effective treatment strategies. Imprint Elvira R. Safiullina, Ekaterina I. Rychkova, Irina V. Мayorova, Diana Kh. Khairutdinova, Anna A. Slonskaya, Anna S. Faronova, Yaroslava A. Davydova, Izobella A. Mussova. Application of digital methods and artificial intelligence capabilities for diagnostics in obstetrics and gynecology. Cardiometry; Issue 27; May 2023; p.111-117; DOI: 10.18137/cardiometry.2023.27.111117; Available from: https://cardiometry.net/issues/no27-may-2023/application-digital-methods

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