ARTIFICIAL INTELLIGENCE IN ENT: OPTIMIZING SURGICAL OUTCOMES IN THYROID CANCER TREATMENT

Abstract

Background: The application of artificial intelligence (AI) may dramatically change the course of surgical operations for thyroid cancer within the ENT speciality. AI can make surgery more precise, help in lowering the chance of adverse events, and help the patient in general. Nevertheless, the sort of Implementation and effectiveness of person-centred care in Mixed Methods patients’ and healthcare practitioners’ experience varies incredibly across healthcare organizations and facilities.
Objective: To this end, the objectives of this work include an evaluation of the implementation of AI in thyroid cancer care the impact of this technology on surgical outcomes concerning the perceptions of healthcare workers, and patients, and the general applicability of AI technology in the healthcare delivery system.
Methods: A cross-sectional and quantitative research design was adopted where structured questionnaires were administered to 250 respondents from the field of ENT including surgeons, medical specialists, and administrators. Views, behaviours, and attitudes of professionals, use of AI, and surgical results data were gathered. The data was analyzed using the Shapiro-Wilk test for normality, Cronbach’s Alpha for reliability, and (inter-observer) agreement was assessed by Fleiss’ Kappa. Exploratory analysis was cross-checked visually using boxplots and correlation heat maps to understand the distribution and other aspects of variables.
Results: In the Shapiro-Wilk test, we found that none of the measures of interest meet the assumption of normality (W = 0.95, p < 0.05), which would mean unevenness in the application and success of AI. The reliability of the developed questionnaire on internal consistency was determined using the Cronbach Alpha coefficient which equaled 0.539 meaning moderate reliability. Five out of six components were rated relatively high in the Fleiss’ Kappa test with a value of -0.079, indicating that respondents had a fair disagreement on the frequency of AI usage about rates of surgery success. The quantitative data further showed that patient satisfaction and perceived AI impact on surgical accuracy was quite variable and preliminary correlation analysis suggested that experience with AI had a positive effect on perceived outcome.
Conclusion: The current significant trend of artificial intelligence shows that its uses in thyroid cancer surgeries have the potential to bring positive results if implemented by various healthcare stakeholders. These fluctuations signal the need to bring about better training, and accessibility to artificial intelligence instruments as well as developing some effective protocols that would facilitate the deployment of artificial intelligence in organizations. These disparities and the potential of AI in ENT surgeries require more research to be carried out to achieve the best results that can be offered by AI.