As an interdisciplinary lab of clinicians and computer scientists, our research spans natural language processing methods development and clinical translational AI efforts. Our core research areas are described below. You can find our full publication list here.

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).

Large Language Model Evaluation and Oversight

Language models learn an implicit knowledge representation from their pretraining data, but the quality, factuality, and biases of this knowledge are imperfect. Our lab is interested in evaluating gaps and inaccuracies in clinical knowledge and understanding in large language models and vision language models, and how we can improve oversight of these models within the clinical workflow.

Selected Papers:

Mining Data from the Electronic Health Records

Our lab develops natural language processing (NLP) methods to automate the extraction of clinical data from the electronic health records. Much of the information collected on patients is entered only in the unstructured free text of clinical notes, radiology reports, and pathology reports – rendering them inaccessible for large-scale analyses and automated monitoring. Informed by our clinical expertise, we develop models that extract information that tends to only be documented in these texts. We are also interested in data augmentation methods to reduce the resources needed to develop these models.

Selected Papers:

Translating AI into the Clinic

We are committed to the evaluation and safe, ethical translation of AI advances into the clinic. We approach this from three angles: Measure, Monitor, and Translate.

Selected Papers:

Ethical and Equitable Implementation of Health Technologies

A common thread throughout our research is promoting socially responsible use of advanced healthcare technologies. Access to new health technologies, including AI, are not distributed equitably and differences in access could amplify the health disparities crisis. We study differences in access to technologies. We also work to define ethical standards for AI implementation that is equitable and protects patients’ rights.

Selected Papers: