Multiple metastases after laparoscopic surgery for early-stage endometrial cancer: A case report

Tsuji, Saori; Hori, Kensuke; Tashima, Lena; Yoshimura, M; Ito, Kimihiko

doi:10.1016/j.ijscr.2020.10.003

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…1). The patient case scenarios were publicly available published data [12][13][14][15][16][17] and then adapted into vignettes and prompts. This step is processed into a question by adding a command sentence and a structured writing system.…”

Section: Methodsmentioning

confidence: 99%

Disparities in medical recommendations from AI-based chatbots across different countries/regions

Gumilar,

Indraprasta,

Hsu

et al. 2024

Preprint

View full text Add to dashboard Cite

This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-hour window. Responses were double-blinded and evaluated on relevance, clarity, depth, focus, and coherence by ten endometrial cancer experts. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight concerning disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This underscores the need for further research and development to ensure equitable access to reliable medical information through AI technologies.

show abstract

Section: Methodsmentioning

confidence: 99%

Disparities in medical recommendations from AI-based chatbots across different countries/regions

Gumilar,

Indraprasta,

Hsu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Disparities in medical recommendations from AI-based chatbots across different countries/regions

Gumilar,

Indraprasta,

Hsu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This emphasizes the necessity for more research and development to guarantee equal access to trustworthy medical information through AI technologies.

show abstract

Multiple metastases after laparoscopic surgery for early-stage endometrial cancer: A case report

Cited by 2 publications

References 15 publications

Disparities in medical recommendations from AI-based chatbots across different countries/regions

Disparities in medical recommendations from AI-based chatbots across different countries/regions

Disparities in medical recommendations from AI-based chatbots across different countries/regions

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