Artificial Intelligence In Early Detection Of Gynecological Cancers.

Abstract

Background: Artificial intelligence (AI) is entering the hot oncology series of clinical diagnostic tools. So far as gynecological malignancies are concerned, timely diagnosis is essential to enhancing survival as well as alleviating the load of therapy. The awareness, acceptance, and the practical issues of AI application in this discipline are, however, yet to be empirically investigated beyond doubt.

Objective: This paper intends to determine the awareness, perception, adoption, and perceived challenges of using AI to detect gynecological cancers early among healthcare providers.

Methods: A quantitative cross-sectional online survey of 280 medical workers was carried out among gynecologists, radiologists, oncologists, and AI specialists. A structured questionnaire, which was composed of 20 Likert scales, was designed and validated. The frequency of normality was tested by the Shapiro-Wilk test. Cronbach's Alpha was used to determine the internal consistency of the study, and Principal Component Analysis (PCA) was used to test construct validity. The data were evaluated in SPSS 25.

Results: The statistical test conducted by Shapiro-Wilk shows that most of the items were not normally distributed, and it is also a characteristic of the ordinal-scale data quality. This, however, did not render the instrument unreliable because the internal consistency was superb, as shown by the figure of Cronbach's Alpha, which was 0.8808. We have seen that the first five components explained 32.63% of the variance according to PCA results, and this statistic implies that the questionnaire has captured more than one dimension of the perception about AI. Most of the respondents showed a positive attitude towards AI, with only technical barriers and implementation support identified as potential issues.

Conclusion: The evidence demonstrates the incidence of knowledge and positive attitudes towards AI at the stage of detecting cancer in gynecology. The questionnaire was expressed to be an effective, reliable, and valid instrument in the measurement of such constructs. In order to adopt the AI tools in the clinical environment, the healthcare systems will have to close the infrastructural and educational gaps to pioneer the ethical and safe implementation practices..