Empowering gynaecologists with Artificial Intelligence: Tailoring surgical solutions for fibroids.

BACKGROUND: In recent years, the integration ofArtificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising potential. Surgical treatment of fibroid is myomectomy if uterine preservation and fertility are the primary aims. AI usage begins with the involvement of LLM (Large Language Model) from the point when a patient visits a gynecologist, from identifying signs and symptoms to reaching a diagnosis, providing treatment plans, and patient counseling. OBJECTIVE: Use of AI (ChatGPT versus Google Bard) in the surgical management of fibroid. STUDY DESIGN: Identifyingthe patient's problems using LLMs like ChatGPT and Google Bard and giving a treatment optionin 8 clinical scenarios of fibroid. Data entry was done using M.S. Excel and was statistically analyzed using Statistical Package for Social Sciences (SPSS Version 26) for M.S. Windows 2010. All results were presented in tabular form. Data were analyzed using nonparametric tests Chi-square tests or Fisher exact test.pvalues < 0.05 were considered statistically significant. The sensitivity of both techniques was calculated. We have used Cohen's Kappa to know the degree of agreement. RESULTS: We found that on the first attempt, ChatGPT gave general answers in 62.5 % of cases and specific answers in 37.5 % of cases. ChatGPT showed improved sensitivity on successive prompts 37.5 % to 62.5 % on the third prompt. Google Bard could not identify the clinical question in 50 % of cases and gave incorrect answers in 12.5 % of cases (p = 0.04). Google Bard showed the same sensitivity of 25 % on all prompts. CONCLUSION: AI helps to reduce the time to diagnose and plan a treatment strategy for fibroid and acts as a powerful tool in the hands of a gynecologist. However, the usage of AI by patients for self-treatment is to be avoided and should be used only for education and counseling about fibroids.

Ovarian Loss in Laparoscopic and Robotic Cystectomy Compared Using Artificial Intelligence Pathology.

A Comparison of Ovarian Loss Following Laparoscopic versus Robotic Cystectomy As Analyzed by Artificial Intelligence-Powered Pathology Software. Background and Objective: To compare the area of ovarian tissue and follicular loss in the excised cystectomy specimen of endometrioma performed by laparoscopic or robotic technique. Methods: Prospective observational study performed between April 2023 to August 2023. There were 14 patients each in Laparoscopic group (LC) and Robotic group (RC). Excised cyst wall sent was for to the pathologist who was blinded to the technique used for cystectomy. The pathological assessment was done by artificial intelligence-Whole Slide Imaging (WSI) software. Results: The age was significantly lower in LC group; the rest of demographic results were comparable. The mean of the median ovarian area loss [Mean Rank, LC group (9.1 ± 15.1); RC (8.1 ± 12.4)] was higher in LC group. The mean of the median total follicular loss was higher in LC group (8.9 ± 9.2) when compared to RC group (6.3 ± 8.9) and was not significant. The area of ovarian loss in bilateral endometrioma was significantly higher in LC group (mean rank 7.5) as compared to RC group (mean rank 3) - (P = .016) despite more cases of bilateral disease in RC group. With increasing cyst size the LC group showed increased median loss of follicles when compared to RC group (strong correlation coefficient 0.347) but not statistically significant (P = .225). AAGL (American Association of Gynecologic Laparoscopists) score did not have any impact on the two techniques. Conclusion: Robotic assistance reduces the area of ovarian and follicular loss during cystectomy of endometrioma especially in bilateral disease and increasing cyst size. It should be considered over the laparoscopic approach if available.