ABOUT US
Learn more about us.
The Bitterman Lab is a research group within the AI in Medicine Program at Mass General Brigham and the Department of Radiation Oncology at Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Harvard Medical School. We are a multidisciplinary team of physician-scientists, informaticians, and AI researchers focused on translating AI advances from the lab into the clinic. We seek to understand how to optimize and implement AI to serve the needs of patients and clinicians - safely, ethically, and responsibly. Our current research focuses on large language models and natural language processing, AI safety and oversight, and clinical trials of AI.
Our current areas of active research include:
- Large language model evaluation and risk mitigation for healthcare.
- Automated data mining from the electronic health records to accelerate cancer care and research.
- Applying natural language processing advances to enhance health literacy and patient-provider communication.
- Translational AI: Clinical trials of AI and frameworks for ethical, patient-centered AI implementation.
Learn more about our Research, Publications, and Team.
The Bitterman lab is grateful to recieve funding for our research from the NIH/NCI, the American Cancer Society (ACS), the American Society for Radiation Oncology (ASTRO), and the American Association for Cancer Research (AACR).
Danielle Bitterman, MD
Principal Investigator
Assistant Professor, Harvard Medical School
Bridging clinical and computer science expertise to translate AI advances into safe, effective, patient-centered healthcare.
Recent Publications.
NEWS AND HIGHLIGHTS
Updates from the lab.
2 min read
Shan Chen awarded Google PhD Fellowship
Lab member receives prestigious 2024 Google PhD Fellowship in Natural Language Processing
BittermanLab
November 26, 2024
5 min read
LLMs and VLMs for healthcare
Highlights from our year of investigations into clinical potentials and risks of LLMs and VLMs
BittermanLab
November 11, 2024
1 min read
Bitterman Lab research is featured in The New York Times!
Reporting on LLMs for patient portal messaging in the newspaper of record
BittermanLab
September 24, 2024
3 min read
Unveiling the fragility of language models to drug names
RABBITS: A new medical robustness investigation and LLM benchmark
BittermanLab
July 19, 2024
3 min read
Introducing Cross-Care
Our new benchmark to assess the healthcare implications of pre-training data on language model bias
BittermanLab
April 30, 2024
7 min read
Large language model assistance
Study out in Lancet Dig Health: How does using LLMs effect patient portal messaging?
BittermanLab
April 24, 2024
5 min read
LLMs for social determinants of health
LLM methods to detect SDoH from unstructured EHR text - published in npj Digital Medicine
BittermanLab
January 11, 2024
