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122: The Role of Generative vs. Non-Generative AI in Medical Diagnostics

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Content provided by Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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In this episode of the Digital Pathology Podcast, I explore the evolving role of Generative vs. Non-Generative AI in Medical Diagnostics. As AI continues to transform the medical field, understanding the differences between these two approaches is essential for pathologists, researchers, and healthcare professionals.

We break down the key concepts behind generative AI models (like ChatGPT and image-generation tools) and non-generative AI models (such as traditional machine learning for diagnostic support). I also highlight a groundbreaking seven-part AI review series published in Modern Pathology, which serves as a crucial reference for integrating AI into pathology.

🔬 Key Topics Covered:

  • [00:00:00] Introduction and Technical Adjustments
  • [00:02:00] Why AI Education in Pathology Is More Important Than Ever
  • [00:04:00] Overview of the Modern Pathology AI Review Series
  • [00:06:00] Generative vs. Non-Generative AI: What’s the Difference?
  • [00:08:00] AI in Pathology: Current Applications and Future Potential
  • [00:12:00] Addressing Bias and Ethical Concerns in AI Models
  • [00:16:00] How AI Can Improve Accuracy in Medical Imaging
  • [00:20:00] The Role of Large Language Models (LLMs) in Pathology
  • [00:25:00] Multi-Modal AI: The Future of Integrating Imaging and Text Data
  • [00:30:00] Real-World Use Cases and AI-Driven Diagnostics

🩺 Why This Episode Matters:
AI is no longer a futuristic concept—it’s here, and it’s shaping the future of digital pathology and medical diagnostics. In this episode, I break down how AI can enhance accuracy, improve workflow efficiency, and make diagnostic insights more accessible. However, AI models also come with risks, such as bias and interpretability challenges, which we need to address responsibly.

🚀 Take Action:
AI in pathology isn’t just a passing trend—it’s a paradigm shift. Whether you're a pathologist, researcher, or lab professional, this episode will give you the knowledge you need to stay ahead in the era of AI-driven diagnostics.

🎧 Listen now and explore the future of AI in pathology!

👉 Watch it here: https://www.youtube.com/live/Mq4Xwxoq_ok?si=o7bA90BlZff9iI_A

#DigitalPathology #AIinHealthcare #PathologyInnovation #GenerativeAI

Support the show

Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

  continue reading

137 episodes

Artwork
iconShare
 
Manage episode 468713263 series 3404634
Content provided by Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Send us a text

In this episode of the Digital Pathology Podcast, I explore the evolving role of Generative vs. Non-Generative AI in Medical Diagnostics. As AI continues to transform the medical field, understanding the differences between these two approaches is essential for pathologists, researchers, and healthcare professionals.

We break down the key concepts behind generative AI models (like ChatGPT and image-generation tools) and non-generative AI models (such as traditional machine learning for diagnostic support). I also highlight a groundbreaking seven-part AI review series published in Modern Pathology, which serves as a crucial reference for integrating AI into pathology.

🔬 Key Topics Covered:

  • [00:00:00] Introduction and Technical Adjustments
  • [00:02:00] Why AI Education in Pathology Is More Important Than Ever
  • [00:04:00] Overview of the Modern Pathology AI Review Series
  • [00:06:00] Generative vs. Non-Generative AI: What’s the Difference?
  • [00:08:00] AI in Pathology: Current Applications and Future Potential
  • [00:12:00] Addressing Bias and Ethical Concerns in AI Models
  • [00:16:00] How AI Can Improve Accuracy in Medical Imaging
  • [00:20:00] The Role of Large Language Models (LLMs) in Pathology
  • [00:25:00] Multi-Modal AI: The Future of Integrating Imaging and Text Data
  • [00:30:00] Real-World Use Cases and AI-Driven Diagnostics

🩺 Why This Episode Matters:
AI is no longer a futuristic concept—it’s here, and it’s shaping the future of digital pathology and medical diagnostics. In this episode, I break down how AI can enhance accuracy, improve workflow efficiency, and make diagnostic insights more accessible. However, AI models also come with risks, such as bias and interpretability challenges, which we need to address responsibly.

🚀 Take Action:
AI in pathology isn’t just a passing trend—it’s a paradigm shift. Whether you're a pathologist, researcher, or lab professional, this episode will give you the knowledge you need to stay ahead in the era of AI-driven diagnostics.

🎧 Listen now and explore the future of AI in pathology!

👉 Watch it here: https://www.youtube.com/live/Mq4Xwxoq_ok?si=o7bA90BlZff9iI_A

#DigitalPathology #AIinHealthcare #PathologyInnovation #GenerativeAI

Support the show

Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

  continue reading

137 episodes

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