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The BioInsights Podcast

A deep dive in to generative AI applications in I-O

March 18, 2024 BioInsights
The BioInsights Podcast
A deep dive in to generative AI applications in I-O
Chapters
Chapters
1:12
Can you tell us briefly about your background in generative AI?
2:02
Can you explain the concept of generative AI and how this differs from other types of machine learning?
8:25
How can generative AI be employed to enhance the analysis of diverse data sets, specifically in the I-O space?
16:58
What are other potential applications of generative AI in the context of experimental cancer medicine?
24:33
Further to that, can you discuss the role of generative AI in optimizing and personalizing treatment strategies for cancer patients participating in clinical trials?
26:32
What considerations should be taken when developing generative AI models in the context of I-O?
32:15
Following this, what challenges have arisen so far, and how should these be addressed moving forward?
34:44
What regulatory challenges and considerations should be considered when implementing generative AI?
38:28
Lastly, what do you see as the future trends and advancements in generative AI for supporting experimental cancer medicine? What emerging technologies could contribute to this?
Info
The BioInsights Podcast
A deep dive in to generative AI applications in I-O
Mar 18, 2024
BioInsights

Lauren Coyle, Commissioning Editor, Immuno-Oncology Insights, speaks with André Freitas, Senior Lecturer (University of Manchester) and Research Group Leader (Idiap Research Institute/National Biomarker Centre), about the intersection of generative AI and experimental cancer treatments, with a focus on clinical trials, regulatory considerations, and leveraging emerging technologies for a transformative impact in I-O. 

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Show Notes Chapter Markers

Lauren Coyle, Commissioning Editor, Immuno-Oncology Insights, speaks with André Freitas, Senior Lecturer (University of Manchester) and Research Group Leader (Idiap Research Institute/National Biomarker Centre), about the intersection of generative AI and experimental cancer treatments, with a focus on clinical trials, regulatory considerations, and leveraging emerging technologies for a transformative impact in I-O. 

1:12
Can you tell us briefly about your background in generative AI?
2:02
Can you explain the concept of generative AI and how this differs from other types of machine learning?
8:25
How can generative AI be employed to enhance the analysis of diverse data sets, specifically in the I-O space?
16:58
What are other potential applications of generative AI in the context of experimental cancer medicine?
24:33
Further to that, can you discuss the role of generative AI in optimizing and personalizing treatment strategies for cancer patients participating in clinical trials?
26:32
What considerations should be taken when developing generative AI models in the context of I-O?
32:15
Following this, what challenges have arisen so far, and how should these be addressed moving forward?
34:44
What regulatory challenges and considerations should be considered when implementing generative AI?
38:28
Lastly, what do you see as the future trends and advancements in generative AI for supporting experimental cancer medicine? What emerging technologies could contribute to this?