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ChatGPT/LLM in Vascular Surgery

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Conteúdo fornecido por Adam Johnson and Powered by the SVS. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Adam Johnson and Powered by the SVS ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

This episode of Audible Bleeding features Drs. Adam Johnson and Jeniann Yi, members of the Society for Vascular Surgery Health Information Technology Committee, to discuss the application of large language models in vascular surgery with two experts in the field, Dr. Andrew Gonzalez and one of his collaborators, Shantanu Dev. The episode promises a conversation exploring machine learning and large language models with insights from the guests' diverse expertise in vascular surgery, health informatics, and artificial intelligence.

Dr. Andrew Gonzalez, an assistant professor in vascular surgery at Indiana University School of Medicine and also an SVS HITC committee member, has clinical and research interests in peripheral arterial disease and artificial intelligence applications for amputation prevention.

Shantanu Dev, a computer science PhD student at Ohio State, focusing on multimodal modeling for clinical applications in AI. Shantanu worked at PWC for eight years in their AI R&D division and co-owns Satsang.ai (www.satsang.ai) Digital Health, an AI company addressing healthcare inequities and quality of care.

Relevant links:

Co-Hosts:

Dr. Jennian Yi is an Assistant Professor of Surgery at the University of Colorado.

Dr. Adam Johnson is an Assistant Professor of Surgery at Duke University, and editor at Audible Bleeding.

Follow us @audiblebleeding

Learn more about us at https://www.audiblebleeding.com/about-1/ and provide us with your feedback with our listener survey.

  continue reading

120 episódios

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ChatGPT/LLM in Vascular Surgery

Audible Bleeding

13 subscribers

published

iconCompartilhar
 
Manage episode 419137412 series 2648498
Conteúdo fornecido por Adam Johnson and Powered by the SVS. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Adam Johnson and Powered by the SVS ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

This episode of Audible Bleeding features Drs. Adam Johnson and Jeniann Yi, members of the Society for Vascular Surgery Health Information Technology Committee, to discuss the application of large language models in vascular surgery with two experts in the field, Dr. Andrew Gonzalez and one of his collaborators, Shantanu Dev. The episode promises a conversation exploring machine learning and large language models with insights from the guests' diverse expertise in vascular surgery, health informatics, and artificial intelligence.

Dr. Andrew Gonzalez, an assistant professor in vascular surgery at Indiana University School of Medicine and also an SVS HITC committee member, has clinical and research interests in peripheral arterial disease and artificial intelligence applications for amputation prevention.

Shantanu Dev, a computer science PhD student at Ohio State, focusing on multimodal modeling for clinical applications in AI. Shantanu worked at PWC for eight years in their AI R&D division and co-owns Satsang.ai (www.satsang.ai) Digital Health, an AI company addressing healthcare inequities and quality of care.

Relevant links:

Co-Hosts:

Dr. Jennian Yi is an Assistant Professor of Surgery at the University of Colorado.

Dr. Adam Johnson is an Assistant Professor of Surgery at Duke University, and editor at Audible Bleeding.

Follow us @audiblebleeding

Learn more about us at https://www.audiblebleeding.com/about-1/ and provide us with your feedback with our listener survey.

  continue reading

120 episódios

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