Join us for the 4th annual Stanford AI+HEALTH Conference, held online on Dec. 10-11, where we'll dive into real-world AI innovations and applications across healthcare. This year's program builds on the insights of thousands of past attendees and is designed to be practical and impactful, delivering actionable knowledge that connects AI innovations with clinical practice. Whether you're a clinician, researcher, innovator, or professional from academia, industry, government, or the non-profit sector, this conference offers valuable opportunities to connect, learn, and explore — wherever you are in your AI journey. Register today: aiplushealth.stanford.edu ✨ Special Offer: The first 100 registrants receive the best conference rate! Early registration ends October 31, 2024. Hosted by the AIMI Center, Stanford Institute for Human-Centered Artificial Intelligence (HAI), and Stanford CME #AIplushealth24 #AIInHealth #HealthInnovation #ArtificialIntelligence #AIMICenter #DigitalHealth #HealthTech #FutureOfHealthcare
Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI)
Higher Education
Palo Alto, California 85,058 followers
On a mission to develop and support transformative medical AI applications
About us
Stanford has established the AIMI Center as a center of excellence to develop, evaluate, and disseminate artificial intelligence systems to benefit all patients. Our center conducts research that solves clinically important medical problems using machine learning and other artificial intelligence techniques.
- Website
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http://aimi.stanford.edu
External link for Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI)
- Industry
- Higher Education
- Company size
- 51-200 employees
- Headquarters
- Palo Alto, California
- Type
- Educational
- Founded
- 2018
Locations
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Primary
1701 Page Mill Rd
Palo Alto, California 94304, US
Employees at Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI)
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Ty Vachon, M.D.
Radiologist | Entrepreneur | Navy Veteran
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Avishkar (Avi) Sharma, MD, CIIP
Director of AI | Body Radiologist | HealthTech Advisor
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Zach Harned
Transactional Counsel for Tech, AI/ML and Digital Health Innovators | Trusted Privacy & IP Advisor
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Edward Korot
Retina Surgeon | Clinical AI Specialist (ex-Google, ex-Genentech/Roche) | co-Founder & CMO Sanro Health
Updates
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Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) reposted this
How are AI tools helping radiologists? Stanford University School of Medicine's Dr. Curtis Langlotz discusses how using radiology data could help maximize accuracy, and more with Icahn School of Medicine at Mount Sinai Department of AI and Human Health’s Dr. Robert Hirten MD at the New Wave of AI in Healthcare 2024. Watch the full video here: https://lnkd.in/gcWAkEZB
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Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) reposted this
There are two related studies: (1) Study of LLMs and physician case management decision making: https://lnkd.in/gQHu8Gpm (2) Study LLMs and diagnostic reasoning: https://lnkd.in/gpBETjqS On the management study (1), performance of the LLM alone was found to be about the same as the performance of the LLM + physicians + conventional resources. Both were better performing than physicians + conventional resources (no LLM access) on the set of medical challenges considered. In the diagnostic study (2), the LLM alone did better than the LLM + physician + conventional. And the LLM + human did about the same as human + conventional resources (without the LLM). That is, the availability of GPT-4 to physicians did not significantly bolster their diagnostic reasoning. The results in both papers indicate that the LLMs (in the context of the scenarios evaluated) are not being harnessed to ideally complement human intellect, per setup, training, context. There's much to do and explore on better training of physicians to use LLMs and, more fundamentally, on methods for advancing human-AI collaboration, in pursuit of dream of leveraging AI to augment human cognition. Stanford Institute for Human-Centered Artificial Intelligence (HAI) Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI)
AI & MEDICAL DIAGNOSES - A new study from Stanford Biomedical Data Science Program has evaluated the impact of GPT-4 on physicians' diagnostic reasoning compared to conventional diagnostic resources: - 50 physicians (attending and resident) participated in a clinical vignette study comparing GPT-4 and traditional diagnostic tools. - Physicians using GPT-4 achieved a median diagnostic score of 76.3%, compared to 73.7% for those using conventional resources—a modest 1.6 percentage point difference. - GPT-4 alone outperformed both groups by 15.5 percentage points in diagnostic reasoning accuracy. - Physicians using GPT-4 spent an average of 82 seconds less on cases compared to the conventional group. - Results suggest opportunities to improve physician-AI collaboration in clinical practice, especially in enhancing diagnostic efficiency. "GPT-4 demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration." — Stanford University Research Team Click below to read the full study 👇that included contributions from Hannah Kerman, Josephine Cool, Zahir Kanjee, Eric Horvitz, Arnold Milstein, Jonathan H. Chen, Adam Rodman, Jason Hom, Neera Ahuja, Yingjie(Isabel) Weng and others. Opinions? Join the conversation on Ethan Mollick's post: https://lnkd.in/dTp63Q_b Ask AI is an independent nonprofit that's been helping people stay one step ahead of artificial intelligence opportunities and disruptions since 2017: Newsletter: https://lnkd.in/gA2aB_Ef Upload demo: https://lnkd.in/gc43EUDu Guest post: https://lnkd.in/gV8wVAVx Fundraising? https://lnkd.in/ga7q5Ned #research #artificialintelligence #AIinHealthcare #medicaldiagnosis #gpt4 #doctors #medicalprofession
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We are igniting AI innovation in medicine! We've teamed up with Amazon Web Services (AWS) to offer Stanford researchers the opportunity to receive AWS credits to stimulate research in the field of artificial intelligence in medicine that leverages cloud capabilities. We're calling for proposals of all sizes for projects at any stage of development from initial to advanced and awarding grants up to $25,000. Applications will be accepted on a rolling basis until all AIMI-AWS credits are allocated. Hurry! Apply before Oct. 31 to benefit from AWS's enhanced technical support. Discover more about our AIMI-AWS Cloud Credit Program: https://lnkd.in/eVz9wujb. A huge thank you to Amazon Web Services for their funding and invaluable support. #AI #Research #Collaboration #AWS #Stanford #HealthcareInnovation
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A dynamic presentation by AIMI Co-director Nigam Shah and an engaging panel discussion featuring AIMI affiliated faculty Fatima Rodriguez, MD, MPH, and Stanford colleagues Alex Weihan Chu and Michael Pfeffer demonstrated the vital role of AI in health care, and emphasized the importance of using it safely, ethically, and cost-effectively. Drs. Shah and Rodriguez's fascinating insights -- shared as part of the "Artificial Intelligence: The Future of Medicine & Health Care Is Here" live event -- showed how Stanford is at the forefront of medical innovation by using AI responsibly for the benefit of the Tri-Valley community and beyond. From the 'Green Button Project' to enhancing patient prognostic models, Dr. Shah discussed how AI is playing a pivotal role in advancing clinical decision-making and patient care at Stanford. He also emphasized the need to ensure benefits of Large Language Models (LLMs) in medical implementations. In the panel discussion, Dr. Rodriguez shared her insights on an initiative that is underway to use an AI model to analyze existing imaging data to find patients at risk of heart disease, and then deliver subsequent—and often lifesaving—preventive care. Want to learn more about the potential of AI to advance health care? Watch Dr. Shah's full presentation: https://bit.ly/3XF6QOu Watch the full panel discussion with Dr. Rodriguez: https://bit.ly/4de3GqA The event included opening and closing remarks by Stanford Health Care Tri-Valley President and CEO Rick Shumway. #Datasets #LargeLanguageModels #AIinHealthcare #InnovationInMedicine #PatientCare
AI Transforms Health Care | Artificial Intelligence: The Future of Medicine & Health Care Is Here
https://www.youtube.com/
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🌟 Newly published! Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems We're delighted to share the latest publication from our co-director Nigam Shah and teams across Technology & Digital Solutions - Stanford Medicine, the Data Science Team at Stanford Health Care, and Stanford University School of Medicine. This groundbreaking paper explores the crucial interplay between AI outputs, decision-making protocols, stakeholder readiness, and their impacts on patient care and operational efficiency. The authors have developed a testing and evaluation mechanism to identify Fair, Useful, and Reliable AI Models (FURMs). Their innovative contributions include usefulness estimates via simulation, financial sustainability projections, and a robust framework for ethical assessments. These tools are now open-source, empowering other healthcare systems to conduct actionable evaluations of AI solutions. Read the paper here: https://lnkd.in/ggZAscA9 Congratulations to the entire team for this significant contribution to advancing AI in healthcare! 🌟 #AIinHealthcare #HealthTech #InnovationInHealthcare #FURMassessment Alison Callahan, Duncan McElfresh, Juan M. Banda, Gabrielle Bunney, MD MBA, Danton Char, Jonathan H. Chen, Conor K. Corbin, Dev Dash, N. Lance Downing, MD, Sneha Shah Jain, MD, MBA, Nikesh Kotecha, Jonathan Masterson, Michelle Mello, Keith Morse, Srikar Nallan, Abby P., Anurang Revri, Aditya Sharma, Christopher Sharp, Rahul Thapa, Michael Wornow, Alaa Youssef, Michael Pfeffer, Nigam Shah
A testing and evaluation mechanism by the Stanford Data Science Team enables a process for accepting, reviewing, and supporting use case requests for artificial intelligence integration in an ethical and structured manner: https://nej.md/3zeENgn Stanford Health Care Alison Callahan Duncan McElfresh Juan M. Banda Nigam Shah
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The RoentGen model, developed by AIMI Center Associate Director Akshay Chaudhari, Director Curtis Langlotz and colleagues, produces medically accurate X-ray images that are nearly indistinguishable from those taken from humans — even according to trained medical professionals. Synthetic data like this has the potential to enhance medical AI by fleshing out incomplete datasets, supplementing data from demographics to eliminate bias and addressing privacy concerns. It's an exciting prospect, but it also introduces several ethical and scientific gray areas. This Stanford Medicine Magazine (Stanford University School of Medicine) article dives deeper into the potential of synthetic data and necessary precautions. Also featuring insights from the AIMI Center affiliated faculty Tina Hernandez-Boussard, James Zou and Olivier Gevaert. Read it here: https://stan.md/3XyEkyQ Stanford Department of Medicine Stanford Radiology #RAISEHealth #GenAI #AIInHealthcare #MedicalAI #RoentGen #SyntheticData #MedicalImaging #Radiology
AI steps into the looking glass with synthetic data
https://stanmed.stanford.edu
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Celebrating the incredible women of our center and beyond who are leading the charge in medical innovation! Through advancements in artificial intelligence and medical imaging, these women are transforming medicine and healthcare while improving patient outcomes. We're so grateful for their passion, knowledge and leadership! #WomenInMedicine #WIMMonth #StanfordWIM #AI #MedicalInnovation #WomenInAI #WomenInStem #WIMM24
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Large language model-powered chatbots show exciting potential to support mental health treatment, but they also come with some high-stakes risks and pose serious safety, legal and ethical concerns. This Stanford Institute for Human-Centered Artificial Intelligence (HAI) Policy Brief outlines a framework developed by Stanford researchers to help evaluate and report on whether AI applications are ready for clinical deployment in behavioral-health settings. The overall goal is to help policymakers and behavioral health practitioners thoughtfully and judiciously integrate the technology into psychotherapy. The AIMI Center is proud to support lead investigator Johannes Eichstaedt's work on LLM-based applications that enhance mental health treatment through our current AIMI-HAI partnership research grant! https://lnkd.in/e8rzB4qU Additional Contributors: Betsy Stade Shannon Wiltsey Stirman Lyle Ungar Cody Boland H. Andrew Schwartz David B. Yaden, PhD João Sedoc Robert DeRubeis Robb Willer Jane Kim #LargeLanguageModels #MentalHealthTreatment #Psychotherapy #Chatbots #AIinHealthcare #MentalHealthTech #BehavioralHealth #AIEthics #HealthTech #AIResearch #StanfordResearch #AIMICenter #HealthcareInnovation #ClinicalDeployment #EthicalAI #PolicyBrief #AIMIHAIpartnership
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Artificial intelligence and machine learning have tremendous potential to impact pediatric research and practice, but these technologies are not yet adapted to the specific needs of children, particularly in lower- and middle-income countries. In this review paper published in PLOS Digital Health, AIMI affiliated faculty Roxana Daneshjou, AIMI researcher Alaa Youssef and colleagues explore barriers to effective AI and machine learning use for children's health across the globe. The team calls for attention to governance concerns, better support for researchers and physicians and consideration for inequities. Read the publication here: https://bit.ly/4dHud0n #HealthAI #MachineLearning #ChildrensHealth #Pediatrics #HealthEquity