Publications by authors named "Pungky Mulawardhana"
Article Synopsis
- The study evaluated the performance of Large Language Models (LLMs), specifically ChatGPT-4, Gemini Advanced, and Copilot, in providing accurate answers to complex gynecologic cancer cases.* -
- Results showed that Gemini Advanced outperformed the other models in accuracy (81.87%), consistency, and depth of responses, while ChatGPT-4 slightly complied better with established treatment guidelines.* -
- The findings suggest that LLMs have the potential to aid clinical practice in gynecologic oncology, but additional refinement is necessary for handling more complex patient scenarios.*
Article Synopsis
- - This study examines the differences and advantages in healthcare information provided by AI chatbots regarding adjuvant therapy for endometrial cancer across four regions: Indonesia, Nigeria, Taiwan, and the USA, and through three platforms: Bard, Bing, and ChatGPT-3.5.
- - An analysis of chatbot responses revealed significant regional variations in quality, with Bing performing the best in Nigeria and Bard showing superior results compared to ChatGPT-3.5 across all areas assessed.
- - The findings underscore the need for further research and development to ensure equitable access to reliable AI-generated medical information, as the quality of information varies widely depending on location and the platform used.
Background: All pregnant women in labor should be universally screened for Coronavirus Disease 2019 (COVID-19) during pandemic periods using reverse transcriptase polymerase chain reaction (RT-PCR) test. In many low-middle income countries, screening method was developed as an initial examination because of limited availability of RT-PCR tests. This study aims to evaluate the screening methods of COVID-19 accuracy in pregnant women.
View Article and Find Full Text PDFObjective: s Data on the clinical manifestations and pregnancy outcomes of pregnant women with COVID-19 are limited, particularly in developing countries. The aim of this study was to analyze the clinical manifestations and pregnancy outcomes in COVID-19 maternal cases in a large referral hospital in Indonesia.
Methods: This study used a prospective cohort design and included all pregnant women with suspected COVID-19.
Background: Ovarian cancer is a gynecological cancer with a higher mortality than other gynecological cancers.
Case Report: There were 43 cases of Indonesian women who died of ovarian cancer in 2015-2017. Patients were first diagnosed at the age of 40-59 years (65.
Objective: The aim of this study is to prove that human umbilical cord mesenchymal stem cell (hUCMSC) therapy on mandibular osteoporotic model is able to increase transforming growth factor-beta-1 (TGF)-β1 expression, Runx2, and osteoblasts.
Materials And Methods: This research is true experimental posttest control group design. Thirty female Wistar rats were divided into 6 groups randomly, which consisted of sham surgery for control (T1), ovariectomy as osteoporotic group (T2), osteoporotic group injected with gelatine for 4 weeks (T3), 8 weeks (T4) injected with hUCMSC-gelatine for 4 weeks (T5) and 8 weeks (T6).