116: DigiPath Digest #18 | Federated Learning in Pathology. Developing AI Models While Preserving Privacy

Send us Fan MailIn today's DigiPath Digest, we delve into federated learning, a decentralized approach to AI training that preserves data privacy. I discuss recent papers from PubMed and share my experiences experimenting with AI tools like Perplexity and Gemini for research efficiency. You will also get updates on upcoming plans, including leveraging AI to share more podcasts with you. Did I mention that this is the last livestream of the year as I head to Poland for Christmas? No More DigiPath Digests. We got to number 18 (I overestimated it a bit in the podcast), and you have been instrumental in continuing this series!Big THANK YOU to all the digital Pathology #TRLBLZRS showing up every Friday morning for this!Join me as we tackle the nuances of federated learning and its impact on healthcare and pathology.00:00 Introduction and Greetings00:18 Today's Topic: Federated Learning00:57 AI Tools and Updates04:39 Federated Learning in Detail08:03 Challenges and Benefits of Federated Learning11:21 Exploring More Papers and Future Plans22:53 Wrapping Up and Final ThoughtsLinks and Resources:Subscribe to Digital Pathology Podcast on YouTubeFree E-book "Pathology 101"YouTube (unedited) version of this episodeTry Perplexity with my referral linkMy new page built with PerplexityPublications Discussed Today:📝 Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine🔗https://pubmed.ncbi.nlm.nih.gov/39610333/📝 A review on federated learning in computational pathology🔗https://pubmed.ncbi.nlm.nih.gov/39582895/If you enjoyed this episode, please subscribe and leave a review on your podcast listening App!Support the showGet the "Digital Pathology 101" FREE E-book and join us!