Abstract technology background of blue lines connected by blue dots.

Machine Learning and Artificial Intelligence

Machine learning and AI-powered solutions have great potential to support busy health care providers. Malone researchers build better algorithms that help providers diagnose disease earlier, operate more precisely, streamline the patient experience, and more.


Narges Ahmidi

Adjunct Assistant Research Scientist

Philippe Burlina

Principal Scientist, Intelligent Systems Center, Johns Hopkins Applied Physics Laboratory

Brian Caffo

Professor, Bloomberg School of Public Health

Nilanjan Chatterjee

Bloomberg Distinguished Professor of Biostatistics and Oncology

Richard Day

Director, Precision Medicine Center of Excellence in Patient Safety and Quality, Armstrong Institute for Patient Safety

Mark Dredze

John C. Malone Professor of Computer Science

Nicholas Durr

Associate Professor of Biomedical Engineering

Gregory D. Hager

Mandell Bellmore Professor of Computer Science

Chien-Ming Huang

John C. Malone Assistant Professor of Computer Science

Ferdinand Hui

Co-Director, Radiology Artificial Intelligence Lab (RAIL)

Tin Yan Alvin Liu

Assistant Professor of Ophthalmology, Johns Hopkins School of Medicine

Michael Oberst

Assistant Research Professor of ComputerScience

Haris Sair

Co-Director, Radiology Artificial Intelligence Lab (RAIL)

Suchi Saria

John C. Malone Associate Professor of Computer Science

Mathias Unberath

Assistant Professor, Department of Computer Science

Swaroop Vedula

Associate Research Professor

Jeremy Waltson

Raymond and Anna Lublin Professor of Geriatric Medicine & Gerontology

Paul Yi

Adjunct Assistant Research Scientist, Malone Center for Engineering in Healthcare

Jithin Yohannan

Assistant Professor of Ophthalmology, Wilmer Eye Institute

Related Projects

Data Science for AI in Healthcare: An Online Course

Machine learning (ML) & artificial intelligence (AI) frameworks are expected to play a significant role in driving innovation and discovery in healthcare research. Examples of ML & AI applications in health care include medical imaging and diagnostics, robot-assisted surgeries, and remote care assistance. To foster research collaborations using ML & AI,...

Noninvasive Localization of Epileptic Seizures

Epilepsy affects nearly 3.5 million people in the United States and is linked to a five-fold increase in mortality. While epilepsy is often controlled with medication, 20-40% of patients are medically refractory and continue to experience seizures in spite of drug therapies. The alternative for...

Predicting Risk for Glaucoma Progression Based Using AI

Glaucoma is the second leading cause of blindness globally, with approximately 79.6 million people expected to be affected by the disease by 2020. Automated visual field (VF) testing remains the gold standard for identifying patients with glaucoma and judging worsening of disease. Approximately 5% of patients with glaucoma undergo rapid worsening of their VF test.

Radiology AI Lab (RAIL)

RAIL is an open structured artificial intelligence focused research collaboration based in the Hopkins Department of Radiology and Radiological Sciences. The group is comprised of physicians and scientists from Johns Hopkins Hospital, the Whiting School of Engineering, and the Applied Physics Laboratory,...

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