• Mashup Score: 3

    In this thought-provoking episode, Dr. Ziad Obermeyer delves into the complex issues of bias, safety, and generalizability of medical AI. Dr. Obermeyer emphasizes the importance of machine learning researchers’ task formulation, an often-overlooked yet significant determinant of bias in AI algorithms. Highlighting the dual impact of machine learning, he compares two of his works that demonstrate how AI can either exacerbate or help mitigate health care disparities. Lastly, he discusses the significant challenges encountered in the development of AI models due to siloed and inaccessible data, sharing his own experiences and solutions in tackling this issue. Dr. Obermeyer is the Blue Cross of California Distinguished Professor at the Berkeley School of Public Health, Co-Founder of Nightingale Open Science, and Co-Founder of Dandelion Health. Transcript    

    Tweet Tweets with this article
    • "You don't want the algorithm to replicate the limitations of human knowledge and the errors built into it. You want it to actually find new signal. So we trained it to listen to the patient..." - @oziadias #RebelHealth https://t.co/nG0TtrweLV

  • Mashup Score: 3

    NEJM AI Grand Rounds, hosted by Arjun (Raj) Manrai, Ph.D. and Andrew Beam, Ph.D., features informal conversations with a variety of unique experts exploring the deep issues at the intersection of artificial intelligence, machine learning, and medicine. You…

    Tweet Tweets with this article
    • RT @UCSF_BCHSI: Listen to the @NEJM AI Grand Rounds w/ @UCSF @atulbutte! https://t.co/ERrgQ0NIC5 An Iron Fist in a Velvet Glove: A Conversa…

  • Mashup Score: 0

    Dr. Lily Peng has driven major medical AI efforts along the long and arduous path from ideation to deployment. From publishing a landmark study in 2016 presenting an AI model to detect diabetic retinopathy in retinal fundus photographs to evaluating deep learning systems in India and Thailand, she has a unique and wide-ranging perspective on both model development and real-world validation. She…

    Tweet Tweets with this article
    • Really enjoying @NEJM_AI podcast, this one on using AI to screen for diabetic retinopathy, can envisage many similar applications in hematopathology! Dr. Lily Peng: AI for Ophthalmology and the Challenges of AI in the Real World https://t.co/tlW9Zn2jbt

  • Mashup Score: 0

    Dr. Marzyeh Ghassemi has been at the forefront of medical machine learning for several years. In this episode, she describes her group’s work and her perspectives on developing and applying machine learning to understand and improve health in ways that are robust, private, and fair. Dr. Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science and the Institute for…

    Tweet Tweets with this article
    • New episode of AIGR with the one and only @MarzyehGhassemi is out! We discuss her research on how our data often does not reflect the better angels of our nature, and what this means for our AI models and for patients: Link: https://t.co/kuLEnqMKEt https://t.co/HHT5FVrQFu

  • Mashup Score: 16

    Large language models (LLMs) like ChatGPT have proven highly capable of a broad array of natural language tasks including summarizing text, generating prose, and answering questions. This episode’s two guests, Dr. Alan Karthikesalingam and Vivek Natarajan of Google, describe their team’s recent efforts to adapt and evaluate LLMs for clinical applications. Alan and Vivek took very different paths…

    Tweet Tweets with this article
    • In the latest episode of AIGR, @arjunmanrai and I go behind the scenes with @alan_karthi and @vivnat to learn about the development of Google's medical LLM, Med-PaLM. We also learn what unique challenges medicine poses for LLMs and their evaluation. Link: https://t.co/4ZW4O0TvT7

  • Mashup Score: 7

    Dr. Pranav Rajpurkar has been at the forefront of medical AI for his entire career. As a graduate student in computer science at Stanford, he created some of the first AI models for radiology and created a suite of datasets and benchmarks that have been widely used by researchers across the world. Now, as a faculty member at Harvard, his group has continued to push the frontier of medical AI…

    Tweet Tweets with this article
    • It's story time! Find me on the other side of the microphone as a guest on the @NEJM_AI Podcast, hosted by @arjunmanrai & @AndrewLBeam. I share lessons learned from my journey as a researcher in AI, and my bets for the future of medical AI. https://t.co/7Eb0LwUJ2v

  • Mashup Score: 2

    Dr. Pranav Rajpurkar has been at the forefront of medical AI for his entire career. As a graduate student in computer science at Stanford, he created some of the first AI models for radiology and created a suite of datasets and benchmarks that have been widely used by researchers across the world. Now, as a faculty member at Harvard, his group has continued to push the frontier of medical AI…

    Tweet Tweets with this article
    • On the next episode of AI Grand Rounds, we sit down with the prodigious Pranav Rajpurkar (@pranavrajpurkar) to talk about AI in radiology, what he learned about mentorship from @AndrewYNg, and the impact of large self-supervised models on medicine. Link: https://t.co/P8ZT76tRzF

  • Mashup Score: 1

    Episode Guest: Euan Ashley, Stanford University professor and cardiologist Hosted by: Andrew Beam and Arjun Manrai Episode Summary: Dr. Euan Ashley is a pioneer. In 2010, he led the team that conducted the first clinical interpretation of a human genome, and he holds the record for the world’s fastest genomic diagnosis. He even has a Guinness World Record to prove it. In this wide-ranging…

    Tweet Tweets with this article
    • The first episode of AI Grand Rounds is out! We sat down with @euanashley to talk about genomics, the future of AI in cardiology, and what he thinks aspiring clinicians should know about AI: https://t.co/mkV54uVz4j