{"podcast":{"title":"From Models to Medicine","slug":"from-models-to-medicine-7769799","podcast_index_feed_id":7769799,"rss_url":"https://anchor.fm/s/110164688/podcast/rss","website_url":"https://podcasters.spotify.com/pod/show/kami-think-tank","image_url":"https://d3t3ozftmdmh3i.cloudfront.net/staging/podcast_uploaded_nologo/45548626/45548626-1773273198170-a0661f37581aa.jpg","author":"KAMI Think Tank","episode_count":19,"summary":"At KAMI Think Tank, we love cutting through the AI hype to showcase where the technology actually stands and how to use it meaningfully within the life sciences. Now, we're bringing that same clarity with our community to a brand new podcast: From Models to Medicine. Every week, we sit down with a real practitioner in the life sciences, whether they're working in the lab, leading innovation at a biotech, or building tools to better serve scientists. We discuss how they cut through the hype of AI and practically leverage the tool in their scientific workflows. Glad to have you here.","last_synced_at":"2026-07-30T04:19:36.234491+00:00","page_url":"https://stenobird.com/podcast/from-models-to-medicine-7769799"},"episode":{"title":"Episode 9: Mitochondria, Machine Learning, and a Few Hard Lessons","slug":"episode-9-mitochondria-machine-learning-and-a-few-hard-lessons","published_at":"2026-05-20T15:39:00+00:00","page_url":"https://stenobird.com/podcast/from-models-to-medicine-7769799/episode-9-mitochondria-machine-learning-and-a-few-hard-lessons","show_page_url":"https://stenobird.com/podcast/from-models-to-medicine-7769799","url":"https://podcasters.spotify.com/pod/show/kami-think-tank/episodes/Episode-9-Mitochondria--Machine-Learning--and-a-Few-Hard-Lessons-e3j9q5f","audio_url":"https://anchor.fm/s/110164688/podcast/play/119907951/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-4-13%2F7c1662ab-76be-19a7-509a-e18286e1a5c7.mp3","summary":"Rachel Jacobson has spent her career moving between some of the most demanding corners of life sciences before founding Powerhouse Biology. In this episode, she traces that journey and explains why, after all of it, she keeps coming back to mitochondria. We get into what it actually takes to bridge biology and machine learning inside a lab culture, why asking "stupid questions" across disciplines is a feature and not a bug, and what she had to unlearn from traditional drug development to work effectively alongside ML engineers. We also dig into data design. Rachel makes a sharp case that data passing standard biological QC is not the same as data that's ready for a machine learning model. Uneven plate layouts, cell debris, different scientists handling samples can all create batch effects that quietly break your model before it ever sees a hypothesis worth testing. She connects all of this to a bigger argument about why human biological variability needs to be built into preclinical pipelines from the start, and why ML might finally give scientists the tools to do that seriously.","meta_description":"Rachel Jacobson has spent her career moving between some of the most demanding corners of life sciences before founding Powerhouse Biology. In this episod…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2623,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/from-models-to-medicine-7769799/episodes/episode-9-mitochondria-machine-learning-and-a-few-hard-lessons/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/from-models-to-medicine-7769799/episode-9-mitochondria-machine-learning-and-a-few-hard-lessons.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}