Diagnostic and Management Performance of ChatGPT in Obstetrics and Gynecology.

OBJECTIVES: The use of artificial intelligence (AI) in clinical patient management and medical education has been advancing over time. ChatGPT was developed and trained recently, using a large quantity of textual data from the internet. Medical science is expected to be transformed by its use. The present study was conducted to evaluate the diagnostic and management performance of the ChatGPT AI model in obstetrics and gynecology. DESIGN: A cross-sectional study was conducted. PARTICIPANTS/MATERIALS, SETTING, METHODS: This study was conducted in Iran in March 2023. Medical histories and examination results of 30 cases were determined in six areas of obstetrics and gynecology. The cases were presented to a gynecologist and ChatGPT for diagnosis and management. Answers from the gynecologist and ChatGPT were compared, and the diagnostic and management performance of ChatGPT were determined. RESULTS: Ninety percent (27 of 30) of the cases in obstetrics and gynecology were correctly handled by ChatGPT. Its responses were eloquent, informed, and free of a significant number of errors or misinformation. Even when the answers provided by ChatGPT were incorrect, the responses contained a logical explanation about the case as well as information provided in the question stem. LIMITATIONS: The data used in this study were taken from the electronic book and may reflect bias in the diagnosis of ChatGPT. CONCLUSIONS: This is the first evaluation of ChatGPT's performance in diagnosis and management in the field of obstetrics and gynecology. It appears that ChatGPT has potential applications in the practice of medicine and is (currently) free and simple to use. However, several ethical considerations and limitations such as bias, validity, copyright infringement, and plagiarism need to be addressed in future studies.

18F-FDG PET/MRI and 18F-FDG PET/CT for the Management of Gynecological Malignancies: A Comprehensive Review of the Literature.

OBJECTIVE: Positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT) or magnetic resonance imaging (18F-FDG PET/MRI) has emerged as a promising tool for managing various types of cancer. This review study was conducted to investigate the role of 18F- FDG PET/CT and FDG PET/MRI in the management of gynecological malignancies. SEARCH STRATEGY: We searched for relevant articles in the three databases PubMed/MEDLINE, Scopus, and Web of Science. SELECTION CRITERIA: All studies reporting data on the FDG PET/CT and FDG PET MRI in the management of gynecological cancer, performed anywhere in the world and published exclusively in the English language, were included in the present study. DATA COLLECTION AND ANALYSIS: We used the EndNote software (EndNote X8.1, Thomson Reuters) to list the studies and screen them on the basis of the inclusion criteria. Data, including first author, publication year, sample size, clinical application, imaging type, and main result, were extracted and tabulated in Excel. The sensitivity, specificity, and diagnostic accuracy of the modalities were extracted and summarized. MAIN RESULTS: After screening 988 records, 166 studies published between 2004 and 2022 were included, covering various methodologies. Studies were divided into the following five categories: the role of FDG PET/CT and FDG-PET/MRI in the management of: (a) endometrial cancer (n = 30); (b) ovarian cancer (n = 60); (c) cervical cancer (n = 50); (d) vulvar and vagina cancers (n = 12); and (e) gynecological cancers (n = 14). CONCLUSIONS: FDG PET/CT and FDG PET/MRI have demonstrated potential as non-invasive imaging tools for enhancing the management of gynecological malignancies. Nevertheless, certain associated challenges warrant attention.

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.

OBJECTIVE: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. MATERIALS AND METHODS: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively. CONCLUSION: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.