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CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
This study presents Chain-of-Diagnosis (CoD) to enhance interpretability in LLM-based medical diagnostics, enabling transparent reasoning and improved performance in diagnosing 9,604 diseases with DiagnosisGPT.
https://arxiv.org/abs//2407.13301
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
View all episodes
By Igor Melnyk
5
33 ratings
This study presents Chain-of-Diagnosis (CoD) to enhance interpretability in LLM-based medical diagnostics, enabling transparent reasoning and improved performance in diagnosing 9,604 diseases with DiagnosisGPT.
https://arxiv.org/abs//2407.13301
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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