Welcome to our lab! We are an inter-disciplinary group of scientists, physicians, and engineers dedicated to leveraging the power of artificial intelligence (AI) to revolutionize healthcare. Our mission is to ensure that AI technologies are fair, trustworthy, and beneficial to all.
We aim to:
- Innovate: Develop cutting-edge AI technologies to address critical healthcare challenges.
- Collaborate: Foster interdisciplinary collaboration to integrate diverse perspectives and expertise.
- Ensure Fairness: Create AI solutions that are equitable and unbiased.
- Build Trust: Develop trustworthy AI systems that patients and healthcare providers can rely on.
Our team comprises experts from various fields, including:
- Scientists: Conducting pioneering research in AI and healthcare.
- Physicians: Bringing clinical insights and ensuring the applicability of AI solutions.
- Engineers: Developing robust and scalable AI technologies.
We are committed to ensuring that our AI technologies are:
- Fair: We rigorously test our AI systems to identify and mitigate biases.
- Transparent: We prioritize explainability and transparency in our AI models.
- Ethical: We adhere to the highest ethical standards in all our research and development efforts.
Our selected publications include:
- Large language models propagate race-based medicine; NPJ Digital Medicine 2023
- Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI; NPJ Digital Medicine 2023
- Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning; NPJ Digital Medicine 2023
- Development and clinical evaluation of an artificial intelligence support tool for improving Telemedicine photo quality; JAMA Dermatology 2023
- Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research; Journal of Investigative Dermatology 2023
- Disparities in dermatology AI performance on a diverse, curated clinical image set; Science Advances 2022
- Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm; Proceedings of the ACM on Human-Computer Interaction 2022
If you wish to contribute to the lab's codebase, please take a look at the Coding Guidelines for more information.
Explore our public repositories on GitHub to see our latest projects and contributions:
By centralizing our guidelines and best practices, we aim to enhance collaboration and maintain high standards across all our projects. Thank you for your cooperation!
