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DigiPath Digest #5 is ready as audio!

We explore how AI and image datasets can accelerate medical education for both radiology and pathology. 

I review comparisons between the GPT-4 vision model and convolutional neural networks for neuropathological changes in the brain. 

We  explore how AI can potentially reduce healthcare costs, particularly in cancer risk discrimination. 

Additionally, there's a focus on AI applications in digital urine cytology for bladder cancer diagnosis. 

I also share personal updates, upcoming podcast guests, and my plans for utilizing YouTube content to create an educational course. 

The episode wraps up with a lively discussion on integrating AI in clinical workflows and prioritizing patient care.

TIMESTAMPS:

00:00 Introduction and Podcast Updates

03:41 Guest Highlights and Personal Updates

06:33 Digital Self-Learning in Radiology

12:14 AI in Breast Cancer Risk Assessment

18:36 Comparing GPT-4 Vision and CNN in Neuropathology

21:58 Challenges in Lesion Identification

22:59 Few-Shot Learning in Neuropathology

24:42 AI in Bladder Cancer Diagnosis

29:48 Innovations in Digital Pathology

38:48 AI-Powered Clinical Workflows

44:42 Conclusion and Future Directions

TODAY'S ABSTRACTS & RESOURCES:

• Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance

• U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer

• Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model

• Evaluating artificial intelligence-enhanced digital urine cytology for bladder cancer diagnosis

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