Generative AI for Healthcare
TLDRDr. Roxanna Daneshjou from Stanford University discusses the impact of generative AI in healthcare, highlighting its potential to revolutionize the field while also addressing the biases and pitfalls of large language models. She emphasizes the need for understanding AI's limitations and the importance of integrating it responsibly into clinical practice.
Takeaways
- 🎓 The Foundations of Biomedical Data Science seminar series at UVA focuses on the application of data science, AI, deep learning, and statistical modeling in biomedical and health sciences.
- 🌟 The theme for the current year is 'Building Partnerships for Generative AI Training in Biomedical and Clinical Research', aiming to explore the potential of AI in healthcare.
- 👩🏫 Dr. Roxanna Danu, an assistant professor at Stanford University, emphasizes the importance of understanding the clinical problems and potential pitfalls of AI tools in healthcare through her research and clinical experience.
- 🏥 The healthcare system is currently facing numerous challenges, and AI has the potential to streamline and improve various aspects, though it is not yet ready to replace physicians.
- 🖼️ Generative AI has been used to create counterfactual images that help in understanding the decision-making process of AI models in dermatology, revealing the features they consider important.
- 🔍 The use of synthetic data in training AI models for healthcare has its limitations, especially in addressing biases related to skin tone and diversity.
- 📈 Large language models like GPT are being used by a significant number of dermatologists for clinical care, highlighting the need for understanding their capabilities and limitations.
- 🤖 AI models can perpetuate false beliefs and biases present in medical training, such as race-based differences in pain thresholds or skin thickness, posing a risk to patient care.
- 🚨 There is a need for more research and education to understand the appropriate use of AI in healthcare, including its limitations and potential for harm.
- 📚 The rapid adoption of AI technologies in healthcare is concerning due to the lack of extensive clinical trials and evaluative frameworks to assess their efficacy and safety.
- 📝 Educators and institutions are tasked with preparing the next generation of healthcare professionals to navigate the complexities of AI integration in clinical practice.
Q & A
What is the main theme of the 2023-2024 Foundations of Biomedical Data Science seminar series?
-The main theme of the seminar series is focused on building partnerships for generative AI training in biomedical and clinical research.
Who is the keynote speaker for this particular seminar?
-The keynote speaker for this seminar is Dr. Roxanna Danu from Stanford University School of Medicine.
What is the significance of the computer vision story in the context of AI and healthcare?
-The computer vision story is significant as it discusses the use of AI tools to predict various medical conditions, the importance of explainable AI, and the potential of generative AI to audit and improve these models.
What challenges does the healthcare system currently face?
-The healthcare system faces challenges such as long wait times for specialist appointments, insurance coverage issues, physician burnout, and overall system inefficiency.
How can AI aid physicians rather than replace them?
-AI can aid physicians by streamlining processes, enhancing diagnostics in primary care settings, and making non-specialists better equipped to handle certain medical conditions, thus reducing the need for specialist care in some cases.
What is the potential impact of generative AI on healthcare?
-Generative AI has the potential to revolutionize healthcare by improving drug discovery, generating synthetic data, creating organ models, and enhancing various other aspects of medical research and practice.
What are the concerns regarding the rapid integration of large language models into healthcare systems?
-The rapid integration raises concerns about the lack of extensive prospective clinical trials, evaluative frameworks, and unanswered research questions regarding the efficacy and safety of these models in healthcare settings.
What was the purpose of the red teaming event held at Stanford?
-The red teaming event aimed to identify potential vulnerabilities, biases, and factual errors in large language models when used in healthcare, by simulating real-world scenarios and assessing the models' responses.
What are the key takeaways from Dr. Roxanna Danu's research on generative AI and healthcare?
-The key takeaways include the potential of generative AI to improve healthcare through auditing and training, the importance of using real and diverse data to avoid biases, and the need for caution and further research when integrating AI into healthcare systems.
What is the role of the audience in the Q&A session of the seminar?
-The audience, especially the biomedical data science Innovation lab participants and alumni, are encouraged to submit questions via the YouTube chat feature, which will be synthesized and asked on their behalf during the last 10 minutes of the lecture.
Outlines
🎤 Introduction and Welcome
The speaker, Jack Van Horn, introduces the 2023-2024 Foundations of Biomedical Data Science seminar series at the University of Virginia. The series is supported by various institutions and focuses on the application of data science, AI, deep learning, and statistical modeling in biomedical and health sciences. This year's theme is on building partnerships for generative AI training in biomedical and clinical research. The speaker announces an upcoming in-person manuscript and grant project development workshop and introduces the keynote speaker, Dr. Roxanna Danu from Stanford University, emphasizing her background and research focus on AI in healthcare.
🏥 Healthcare System Challenges and AI's Potential
Dr. Roxanna Danu discusses the challenges in the current healthcare system, using the example of a beach scenario to illustrate the difficulties in accessing dermatologist care. She emphasizes the broken nature of the system and the potential for AI to streamline processes. While acknowledging the sensational headlines about AI surpassing doctors, she advocates for AI as a tool to aid physicians. She also highlights the rapid integration of AI in healthcare, particularly large language models, without extensive clinical trials or evaluative frameworks.
🖼️ Generative AI and its Impact on Healthcare
Dr. Danu delves into the impact of generative AI on healthcare, discussing its potential applications in drug discovery, synthetic data generation, and more. She notes the significant leap in capabilities with the advent of models like GPT-3.5 and GPT-4. The speaker also touches on the importance of evaluating these models for efficacy and the need for research in this area. She introduces four stories about generative AI in healthcare, focusing on computer vision and large language models, and their application in research and practice.
🔍 Auditing Computer Vision Models with Generative AI
Dr. Danu explains the use of generative AI to audit computer vision models in healthcare. She discusses the issue of models being black boxes and the importance of understanding the features they use for decision-making. The speaker describes a methodology using generative AI to create counterfactuals, which helps in assessing the reasoning processes of the models. This approach reveals factors influencing AI decision-making in a clinically interpretable language.
📈 Addressing Bias in AI with Synthetic Data
The speaker addresses the use of synthetic data to train models and its potential to address bias in AI. She discusses the lack of diverse skin tone representation in training data and the potential for synthetic images to augment models. However, she warns that an imbalance in using synthetic images can perpetuate bias. Dr. Danu emphasizes the need for more real images across diverse skin tones to build fair models.
💬 Physician Perspectives on Large Language Models
Dr. Danu shares insights from a survey on dermatologists' use of large language models in clinical care. The survey reveals that a significant number of dermatologists use these models for clinical decision-making, with many using them daily. The speaker discusses the types of tasks for which these models are used and the perceived accuracy of their responses. She also addresses the concerns about the potential biases and inaccuracies in large language models and the need for awareness and education on these issues.
🛑 Red Teaming Event: Uncovering Model Vulnerabilities
The speaker describes a red teaming event held at Stanford to explore the vulnerabilities of large language models in healthcare. The event involved interdisciplinary teams using the models in simulated clinical scenarios to identify potential safety, privacy, factual accuracy, and bias issues. The results showed a significant number of inappropriate responses, highlighting the risks of using these models in healthcare without proper understanding and safeguards.
🤖 AI Integration in Medicine: Benefits and Risks
Dr. Danu concludes her talk by highlighting the potential of AI to improve healthcare models through auditing and synthetic image training, as well as the benefits of large language models in clinical care. However, she also emphasizes the potential risks of inaccuracies and harm. The speaker calls for a nuanced perspective on the use of AI in medicine, advocating for awareness of biases and the need for continued research and education in this rapidly evolving field.
🗣️ Final Q&A and Closing Remarks
The host expresses gratitude to Dr. Danu for her insightful presentation and engages in a discussion on the potential dangers of self-diagnosis through AI and the need for public awareness. The conversation touches on the importance of understanding biases and the risks associated with the rapid adoption of AI in healthcare. Dr. Danu emphasizes the need for education and the responsibility of informing patients about algorithmic decision-making in healthcare.
Mindmap
Keywords
💡Biomedical Data Science
💡Generative AI
💡Healthcare System
💡Artificial Intelligence
💡Clinical Experience
💡Dermatology
💡Bias in AI
💡Electronic Health Records (EHR)
💡Red Teaming
💡Healthcare Innovation
💡Explainable AI
Highlights
The Foundations of Biomedical Data Science seminar series, supported by UVA's institutes and the National Institutes of General Medical Sciences, focuses on data science methodologies, AI, deep learning, and statistical modeling in biomedical and health sciences.
The 2023-2024 theme is on building partnerships for generative AI training in biomedical and clinical research, aiming to explore the issues, promises, opportunities, and challenges associated with AI applications in biomedical science.
Dr. Roxanna Danu, an assistant professor at Stanford University, discusses the potential of generative AI to revolutionize healthcare, including the use of large language models and their biases and pitfalls.
Generative AI has made significant advancements, with GPT 3.5 and GPT 4 representing a monumental leap in capabilities, impacting various areas in healthcare from drug discovery to synthetic data generation.
The healthcare system is facing challenges such as physician burnout and difficulty accessing specialty care, highlighting the need for AI to streamline and enhance primary care diagnostics.
Dr. Danu's research lab focuses on applying fair and transparent AI in healthcare, emphasizing the importance of understanding the clinical problems and potential issues with AI tools from a frontline medical perspective.
Explainable AI is crucial for understanding the decision-making process of algorithms, with methods like saliency maps helping to identify important features in the model's decision-making process.
Generative AI can create counterfactuals to assess model reasoning, revealing factors that influence AI decision-making in a clinically relevant and interpretable manner.
Synthetic images can augment AI model training but may not solve bias issues; a balanced mix of real and synthetic images is necessary to ensure fair and accurate models.
A survey study found that 65% of dermatologists have used large language models in clinical care, with 85% using Chat GPT, highlighting the rapid and widespread adoption of these technologies in healthcare.
Large language models can perpetuate false race-based medical beliefs, such as differences in pain thresholds or skin thickness between races, posing a risk for reinforcing incorrect medical practices.
A red teaming event at Stanford aimed to identify vulnerabilities in large language models used in healthcare, revealing a 20% rate of inappropriate responses, including factual inaccuracies and biases.
Generative AI and WebMD-like technologies enable self-diagnosis by the public, raising concerns about the potential for misinformation and confirmation bias, as well as the benefits of increased healthcare access and awareness.
The rapid adoption of AI technologies in healthcare raises concerns about the potential for algorithmic harm and the need for a framework to test, monitor, and regulate these tools.
Educators and professional societies must adapt to the fast pace of AI development to provide proper training and education for the next generation of healthcare professionals and ensure the safe and effective use of AI in medical practice.