Sandra Villiger’s Post

HealthNext 2024 is less than one month away and the program is live now on our website. You can now view the complete lineup of speakers, fireside chats, panel discussions, and keynote addresses, covering topics like funding AI applications in clinical care and establishing trust and transparency in the age of AI. Don’t miss out on these important conversations, follow the link on our website to get tickets. #HealthNext24

Considering Biased Data as Informative Artifacts in AI-Assisted Health Care "The growing attention to bias within the AI and health care communities is a welcome development, especially as we continue to experience the ebbs and flows of the coronavirus disease 2019 pandemic. However, the harms of AI have often been imprecisely and narrowly considered as a data bias problem. Although there is value in innovating computational ways of altering data sets and engaging diverse participants in biomedical research, these cannot be the only solutions, and they should not rely on the implicit notion that past and current health data have little to offer AI research and development today." https://lnkd.in/e-z7e5eV

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

Our dear Prof. Nigam Shah is AMAZING as always! ✨ Wonderful session by Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). The future of medicine and healthcare is here, indeed. #KPJU #KPJHealthcareUniversity #StanfordAIMI #AIMedicine #DigitalHealth #FifthGear

AI and Clinical Practice: Using AI to Increase Access to Reliable Health Information

A recent publication in the New England Journal of Medicine shed light on the complexities of AI in medicine. Kadija Ferryman, Ph.D., Maxine Mackintosh, Ph.D., and Marzyeh Ghassemi, Ph.D., argue for a paradigm shift in how we perceive clinical data. Rather than viewing biased data merely as a problem to be fixed, they suggest treating these data sets as informative artifacts. This perspective enables us to uncover underlying values, practices, and patterns of inequity in health care systems. By examining clinical data through this lens, we can gain valuable insights into the social and cultural dimensions of health care, ultimately guiding the development of more equitable AI tools. This work emphasizes the importance of interdisciplinary approaches in AI research, blending data science with social science to enhance the integrity and inclusivity of health care technologies. How can we leverage such insights to improve AI applications in health care, ensuring they serve all segments of society equitably? #AIinMedicine #HealthCareInnovation #DataEquity #DigitalHealth #NEJM https://lnkd.in/dEJ6Zsga

This spring, Stanford University School of Medicine and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) co-hosted the inaugural RAISE Health Symposium, bringing together top minds to discuss how AI can be responsibly implemented in health care. This summary paper highlights how these advancements bring both new possibilities and challenges. Explore the findings, and how we can proactively address them, in the link below:   https://lnkd.in/gykteeFC   #AI

https://lnkd.in/eEYECR_5 Very fascinating and eye opening episode. This particular conversation was the perfect intersection of my studies in science & health and my career as a Data Professional. It's always a lively discussion discussing Healthcare, let alone the system that currently exists in the US, but Dr. Attia and Dr. Kohane bring up interesting ideas that could be game changing. #thedrive #ai #healthcare

Can AI and healthcare unite for a healthier future? Discover insights from Purdue University College of Health and Human Sciences Cody Mullen, Ph.D., Program Director of #Purdue’s online Master of Health Administration. Dr. Mullen discusses the transformative role of AI in healthcare. As medical organizations bridge staffing gaps, AI contributes to diagnostics, mental health support and more. Explore how AI is reshaping the healthcare landscape. ➡️ https://purdue.biz/3HamFF3

🎉 Congratulations to Associate Professor Nan Liu, Director of Duke-NUS AI + Medical Sciences Initiative (DAISI), on receiving the High Impact Paper Award 2023 for his and his team's work on Large Language Models (LLM) in Healthcare! The team's paper, which was published in Health Care Science last year, delineates LLM's potential applications in clinical contexts, spanning pre-consultation, disease diagnosis, management, and education. While acknowledging their transformative potential, the team also highlight concerns surrounding privacy, hallucinations, biases, and the opaque nature of their decision-making processes, potentially impeding their widespread adoption in patient care. Harnessing LLMs ethically and responsibly is paramount as they reshape myriad facets of our world. Check out the full published paper from the link in the comments. #DukeNUS #MakeGreaterThingsHappen #Achievement #AI #LargeLanguageModels