Emily Alsentzer
@Emily_Alsentzer
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Assistant Professor @Stanford in Biomedical Data Science and (by courtesy) CS. Trustworthy, deployable ML for healthcare. Prev @HarvardMed @mit_hst @MIT_CSAIL.
Palo Alto, CA
Joined June 2012
RT @MonicaNAgrawal: General LLMs perform well on clinical NLP tasks, even though they never see EHR data. How?. Our CHIL 2025 paper, led by….
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RT @SymposiumML4H: 🚨 Machine Learning for Health (ML4H) is back and better than ever!.🌴 Join us in San Diego on December 1–2, 2025, right b….
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RT @davidjhwu: Packed house at #AIMI25 for this star-studded panel featuring @Emily_Alsentzer (Stanford CS), @thekaransinghal (OpenAI), @Kh….
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RT @CHILconference: 🚨 Calling all health AI founders & builders!.Join us at Health AI Builders: A CHIL Unconference — June 25th @ UC Berkel….
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RT @rajiinio: In the absence of federal action, the states have led the way for years on pushing forward common sense AI policy. This is re….
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RT @arjunmanrai: #SAIL2025 was a terrific meeting and this sample of anonymized quotes collected by @irenetrampoline includes a level of c….
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RT @AlyssaUnell: 7/🧵Thanks to my many collaborators on this project, including @HennyJieCC, @chenbowen118, @drnigam, @Emily_Alsentzer, @san….
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RT @AlyssaUnell: 1/🧵Introducing TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records. When we evaluate LLM….
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RT @percyliang: 1/🧵How do we know if AI is actually ready for healthcare? We built a benchmark, MedHELM, that tests LMs on real clinical ta….
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RT @NEJM_AI: Dr. @Emily_Alsentzer, a @Stanford faculty member and expert in clinical AI, discusses the evolution of natural language proces….
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RT @NEJM_AI: To move forward as a field, clinicians need to design benchmarks that better align with the tasks LLMs are expected to perform….
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RT @adamatw: @Emily_Alsentzer Outstanding paper — couldn’t agree more! @VUMC_VCLIC is always happy to partner with researchers on real-worl….
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See the full paper here: Had a wonderful time collaborating with the incredible @rajiinio and @RoxanaDaneshjou.
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Medical licensing exams are convenient LLM benchmarks, but they don’t reflect real-world clinical tasks. With LLMs already in EHRs, we need benchmarks that match real-world needs. Let’s partner with hospitals piloting these tools to develop diverse, task-specific evaluations.
In medical settings, researchers & institutional users often cite medical examination benchmark results as a proxy for AI model performance across a range of medical tasks. This is a woefully misguided practice - as with actual doctors, test performance != clinical competence!
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RT @HarvardDBMI: Reminder: Abstract submissions due next Friday 1/17 for #SAIL25 May 6–9 in Río Grande, Puerto Rico! In-person attendance l….
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RT @CALonghurst: 𝐒𝐡𝐨𝐮𝐥𝐝 𝐭𝐡𝐞 𝐅𝐃𝐀 𝐚𝐩𝐩𝐫𝐨𝐯𝐞 𝐀𝐈 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬?. Let's review using sepsis as a case study 🔥. The @US_FDA reviews AI/ML tools under….
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RT @marinkazitnik: Medicine thrives on knowledge, yet clinical vocabularies are fragmented. AI struggles to unify this knowledge, creating….
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RT @HarvardDBMI: CALL FOR ABSTRACTS | SAIL 2025 on May 6–9 in Río Grande, Puerto Rico! In-person attendance limited to those with accepted….
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Submit an abstract to join us in Puerto Rico! . ✨focus on real-world deployment of AI into the clinic with practical, in-depth discussions .✨intimate setting for detailed conversations & networking.✨beachfront hotel 🌴.
The SAIL 2025 CALL FOR ABSTRACTS is open! Join us May 6-9 in Puerto Rico! In-person attendance is limited to those with accepted work. @NEJM_AI will award scholarships to top abstracts. Travel support is also available. Submit here by January 17: #SAIL25
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