Generative AI for Healthcare
TLDRIn this insightful seminar, Dr. Roxanna Daneshjou from Stanford University discusses the transformative potential of generative AI in healthcare, particularly in dermatology. She explores the use of AI in clinical decision-making, the importance of diverse training data to avoid bias, and the challenges of integrating AI tools into healthcare systems. Dr. Daneshjou emphasizes the need for careful evaluation of AI models, highlighting the risks of algorithmic harm and the importance of transparency in healthcare decision-making.
Takeaways
- 🎓 The Foundations of Biomedical Data Science seminar series, supported by UVA and NIGMS, focuses on data science methodologies, AI, deep learning, and statistical modeling in biomedical and health sciences.
- 🌟 The theme for this year is 'Building Partnerships for Generative AI Training in Biomedical and Clinical Research', aiming to explore the potential of AI in revolutionizing healthcare.
- 👩🏫 Dr. Roxanna Danu, an assistant professor at Stanford University, emphasizes the importance of understanding both data science and clinical practice to address healthcare challenges effectively.
- 🏥 AI has the potential to streamline the healthcare system, but it is not yet at the stage of replacing physicians. Instead, it should be seen as a tool to aid healthcare professionals.
- 🖼️ Generative AI has made significant advancements, with models like GPT-3.5 and GPT-4 showing a leap in capabilities. However, there are still limitations and biases that need to be addressed.
- 🔍 The use of explainable AI and generative AI can help in auditing and understanding the decision-making processes of AI models in healthcare, revealing factors that influence AI decisions.
- 📈 The lack of diverse data in AI training can lead to biased models. For instance, models trained primarily on white skin may not perform as well on skin lesions of other skin tones.
- 💡 Large language models like ChatGPT are being used by a significant number of dermatologists in clinical care, highlighting the need for understanding their potential biases and inaccuracies.
- 🚨 A red teaming event at Stanford aimed to identify vulnerabilities in large language models used in healthcare, revealing issues related to safety, privacy, factual inaccuracies, and biases.
- 📚 Education and training for the next generation of healthcare professionals must include understanding the capabilities and limitations of AI to ensure safe and effective use 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 Dr. Roxanna Danu's area of expertise?
-Dr. Roxanna Danu is an assistant professor of biomedical data science and dermatology at Stanford, with a focus on the application of fair and transparent artificial intelligence for healthcare.
What is the purpose of the Biomedical Data Science Innovation Lab program?
-The purpose of the program is to leverage seminar presentations as vital material for participants to develop manuscripts and grants in a culminating in-person workshop, fostering partnerships between AI platform developers, biomedical researchers, and university educators.
How does the healthcare system's current state inspire the exploration of AI in healthcare?
-The healthcare system is considered broken in many ways, with issues like long wait times, physician burnout, and difficulty accessing specialty care, which has led to the exploration of AI as a potential solution to streamline and improve these processes.
What are the potential applications of large language models in healthcare?
-Large language models can be used in various aspects of healthcare, including drug discovery, synthetic data generation, and improving the efficiency of electronic health records.
What concerns does Dr. Danu raise about the rapid integration of AI into healthcare?
-Dr. Danu raises concerns about the lack of extensive prospective clinical trials and evaluative frameworks to understand the efficacy of AI models in healthcare, as well as the potential for AI to perpetuate biases and inaccuracies.
What is the significance of the red teaming event held at Stanford?
-The red teaming event aimed to identify vulnerabilities, biases, and potential factual errors in large language models when used in healthcare settings, to ensure the safe and effective implementation of these technologies.
What are the key takeaways from Dr. Danu's research on generative AI and computer vision in healthcare?
-Dr. Danu's research highlights the potential of generative AI to audit and improve computer vision models in healthcare, but also emphasizes the need for diverse and representative training data to avoid biases and ensure accurate decision-making.
What advice does Dr. Danu give regarding the use of AI tools in clinical practice?
-Dr. Danu advises clinicians to be aware of the potential biases and limitations of AI tools, to not fully rely on them without critical thinking, and to always consider the need for human review in decision-making processes.
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. He acknowledges the support from various institutions and highlights the focus on data science, AI, deep learning, and statistical modeling in biomedical and health sciences. The theme for the year is on building partnerships for generative AI training in biomedical and clinical research. The speaker expresses excitement for the program and introduces the first 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 current challenges in the American healthcare system, using the example of a person discovering a new lesion at the beach and the subsequent difficulties in getting timely medical attention. She emphasizes the need for AI to streamline healthcare and discusses the tension between AI replacing physicians versus aiding them. Dr. Danu also touches on the rapid advancements in AI and its potential applications in healthcare, including large language models and computer vision, while acknowledging the need for careful integration and evaluation.
🧠 Exploring Generative AI in Healthcare
Dr. Danu delves into the specifics of generative AI, including its impact on healthcare through drug discovery, synthetic data generation, and organ generation. She discusses the excitement around large language models and the rapid integration of AI into healthcare systems, despite the lack of extensive clinical trials and evaluative frameworks. Dr. Danu shares her concerns about the speed of adoption and the need for thorough research and understanding of AI models and their efficacy.
🔍 Computer Vision in Healthcare Research
Dr. Danu presents two stories related to computer vision in healthcare. The first involves using generative AI to audit and understand the decision-making process of AI models, particularly in identifying relevant features for diagnosing skin conditions. The second story focuses on the use of synthetic images to train AI models, highlighting the importance of diverse and representative data sets to avoid biases and improve model accuracy.
📈 Survey on Physician Use of Large Language Models
Dr. Danu discusses a survey she conducted to understand how dermatologists are using large language models in clinical care. The results show that a significant number of dermatologists are already using these models for clinical decision-making, with a majority finding the models somewhat accurate. She emphasizes the need for physicians to understand the potential biases and inaccuracies of these models and the importance of real images in training to ensure fairness and accuracy.
🚨 Red Teaming Event: Identifying Vulnerabilities in AI
Dr. Danu describes a red teaming event held at Stanford to identify vulnerabilities in large language models used in healthcare. The event involved interdisciplinary teams using the models in simulated clinical scenarios and labeling the responses based on safety, privacy, factual inaccuracies, and bias. The results showed a concerning number of inappropriate responses, highlighting the potential risks of using AI in healthcare without proper understanding and safeguards.
📝 Closing Thoughts and Future Considerations
In the concluding part, Dr. Danu shares her key takeaways from the discussion. She acknowledges the potential of AI to improve healthcare models and assist in clinical care, while also cautioning about the potential for inaccuracies and harm. She emphasizes the need for ongoing education and research to ensure the safe and effective integration of AI into healthcare practices. Dr. Danu's research and insights provide a comprehensive look at the current state and future directions of AI in healthcare.
Mindmap
Keywords
💡Biomedical Data Science
💡Generative AI
💡Healthcare System
💡Artificial Intelligence (AI)
💡Computer Vision
💡Large Language Models
💡Bias in AI
💡Clinical Decision-Making
💡Healthcare Technology Adoption
💡Explainable AI
Highlights
The Foundations of Biomedical Data Science seminar series focuses on the application of data science methodologies, artificial intelligence, 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.
Generative AI has the potential to revolutionize healthcare through its various applications.
Large language models are being integrated into healthcare, with companies like Epic and Microsoft partnering to bring GP4 to electronic health records.
There is a lack of extensive prospective clinical trials and evaluative frameworks for understanding the efficacy of AI models in healthcare.
Explainable AI is crucial for understanding what factors influence algorithmic decision-making in a clinically relevant manner.
Generative AI can be used to create counterfactuals to audit and understand the reasoning processes of AI models in healthcare.
The use of synthetic data in healthcare AI model training can help but may also lead to continued bias if not properly balanced with real data.
A survey study revealed that 65% of dermatologists have used large language models in clinical care, with 85% using ChatGPT.
Large language models can perpetuate false race-based beliefs in medicine, highlighting the need for vigilance and education on their use.
A red teaming event at Stanford aimed to identify vulnerabilities in the use of large language models in healthcare, revealing a 20% rate of inappropriate responses.
The adoption of AI technologies in healthcare is happening rapidly, raising concerns about the potential for algorithmic harm and the need for proper oversight.
Patients have the right to use AI tools for self-diagnosis, but there is a need for awareness of potential confirmation biases and misinformation.
The speaker advocates for transparency in healthcare decision-making when algorithms are used, emphasizing the importance of human review.
Education and training for the next generation of healthcare professionals must include understanding the limitations and appropriate applications of AI algorithms.
The speaker's research lab focuses on the application of fair and transparent artificial intelligence for healthcare, emphasizing the importance of addressing biases and ensuring algorithmic fairness.
The healthcare system is currently facing challenges such as physician burnout and difficulties in accessing specialty care, which AI could potentially help streamline.
The speaker discusses the importance of not replacing physicians with AI but rather using AI to aid physicians in providing better care.
The speaker's work on using generative AI to understand model decision-making and the need for real, diverse datasets to ensure fair AI in dermatology.