Lotter Lab | Research

Distinguishing between Rigor and Transparency in FDA Marketing Authorization of AI-enabled Medical Devices

Abdul Rahman Diab, William Lotter

Radiology: Artificial Intelligence 2025

Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs

Advait Gosai*, Arun Kavishwar*, Stephanie L. McNamara, Soujanya Samineni, Renato Umeton, Alexander Chowdhury, William Lotter

Proceedings of Machine Learning for Health (ML4H) 2025

pdf | arXiv

Comparing Computational Pathology Foundation Models using Representational Similarity Analysis

Vaibhav Mishra, William Lotter

Proceedings of Machine Learning for Health (ML4H) 2025

pdf | arXiv | code

Influence of Mammography Acquisition Parameters on AI and Radiologist Interpretive Performance

William Lotter, Daniel S. Hippe, Thomas Oshiro, Kathryn P. Lowry, Hannah S. Milch, Diana L. Miglioretti, Joann G. Elmore, Christoph I. Lee, William Hsu

Radiology: Artificial Intelligence 2025

paper

AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics

Stella Su, Marc Harary, Scott J. Rodig, William Lotter

In Review 2025

pdf | arXiv

How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations

Rohan Singh, Monika Bapna, Abdul Rahman Diab, Emily S. Ruiz, William Lotter

npj Digital Medicine 2025

paper

Pan-cancer spatial characterization of key immune biomarkers in the tumor microenvironment

James R. Lindsay, Jennifer Altreuter, Joao V. Alessi, Jason L. Weirather, Anita Giobbie-Hurder, Ian Dryg, Katharina Hoebel, Bijaya Sharma, Kristen Felt, F. Stephen Hodi, Neal Lindeman, Lynette M. Sholl, Ethan Cerami, Jonathan A. Nowak, Mark M. Awad, Scott J. Rodig, William Lotter

Cell Reports Medicine 2025

paper | code

Acquisition parameters influence AI recognition of race in chest x-rays and mitigating these factors reduces underdiagnosis bias

William Lotter

Nature Communications 2024

paper | code

Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion

Marc Harary, Eliezer M. Van Allen, William Lotter

Findings Track, Machine Learning for Health (ML4H) 2024

pdf | arXiv | code

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

William Lotter, Michael J. Hassett, Nikolaus Schultz, Kenneth L. Kehl, Eliezer M. Van Allen, Ethan Cerami

Cancer Discovery 2024

paper

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation

Stephanie L. McNamara, Paul H. Yi, William Lotter

npj Digital Medicine 2024

paper | code

Beyond Structured Attributes: Image-Based Predictive Trends for Chest X-Ray Classification

Katharina V Hoebel*, Jesseba Fernando*, William Lotter

Medical Imaging with Deep Learning 2024

pdf | code

Representing visual classification as a linear combination of words

Shobhit Agarwal, Yevgeniy R. Semenov, William Lotter

Proceedings of Machine Learning for Health (ML4H) 2023

pdf | arXiv

Publications

Distinguishing between Rigor and Transparency in FDA Marketing Authorization of AI-enabled Medical Devices

Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs

Comparing Computational Pathology Foundation Models using Representational Similarity Analysis

Influence of Mammography Acquisition Parameters on AI and Radiologist Interpretive Performance

AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics

How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations

Pan-cancer spatial characterization of key immune biomarkers in the tumor microenvironment

Acquisition parameters influence AI recognition of race in chest x-rays and mitigating these factors reduces underdiagnosis bias

Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation

Beyond Structured Attributes: Image-Based Predictive Trends for Chest X-Ray Classification

Representing visual classification as a linear combination of words

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