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 advance the science and clinical translation of foundation models for healthcare. Our work spans algorithm development, evaluation, and real-world deployment, with an emphasis on safety, reliability, and clinical utility. As a multidisciplinary team of physician-scientists and computer scientists, we ground methodological innovations in high-impact medical applications.
Our areas of active research include:
- Foundation model evaluation, alignment, and oversight: Developing principled evaluation frameworks, alignment objectives, stress-testing methodologies, and oversight architectures to characterize and control foundation model behavior in healthcare settings.
- Oncology information extraction from EHRs: Building NLP pipelines and representation learning methods to derive structured signals from heterogeneous cancer clinical texts for downstream modeling and causal inference.
- AI for patient-facing education and clinical decision-support in oncology: Building AI-powered systems that produce accurate, personalized educational materials and integrate actionable insights into clinician workflows.
- Translational AI and clinical evaluation: Executing clinical studies of AI systems, analyzing human–AI interaction and failure modes, and developing deployment frameworks that satisfy regulatory, ethical, and workflow constraints.
Learn more about our Research, Publications, and Team.

Danielle Bitterman, MD
Principal Investigator
Assistant Professor, Harvard Medical School
Clinical Lead for Data Science/AI, Mass General Brigham Digital
Dr. Bitterman is a physician–scientist specializing in the safety and evaluation of clinical AI systems, with emphasis on natural language processing and emerging foundation models. As a practicing radiation oncologist and health system AI leader, she integrates frontline clinical insight with rigorous methodological approaches to assess model performance, reliability, and risk. Her lab also develops translational frameworks and conducts prospective AI testing to guide the safe, equitable, and responsible deployment of advanced AI in patient care.
Recent Publications.
NEWS AND HIGHLIGHTS
Updates from the lab.

4 min read
LLM sycophancy research recieves media coverage
Manuscript demonstrating LLM risks in healthcare featured in STAT, NYT
BittermanLab
November 22, 2025

2 min read
PCORI funding awarded to advance AI for trials
Project will develop and test patient-centered, AI-enabled informed consent
BittermanLab
July 16, 2025

3 min read
Lab recieves NIH-NCI funding on AI for immunotherapy toxicity
New 5-year grant will research novel AI approaches to improve cancer survivorship
BittermanLab
March 22, 2025

1 min read
Lab awarded ACS-ASTRO grant
Our new project leverages NLP to understand long-term side effects of radiotherapy
BittermanLab
January 31, 2025

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

