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Johns Hopkins researchers successfully taught an AI simulation to close incisions by breaking the complicated suturing process down into simpler subtasks.

Johns Hopkins researchers release a new standard for evaluating medical AI models to promote fairness and reduce bias.

Supported by a Challenge Grant from the Johns Hopkins Institute for Assured Autonomy, a team led by Chien-Ming Huang is investigating the conceptual and technological advances required to enable artificial agents to act with prosocial intent.

Johns Hopkins researchers used artificial intelligence to create the largest, most comprehensive dataset of abdominal organs to date to help radiologists quickly and accurately identify tumors and other diseases.

Johns Hopkins computer scientists explore how gendered voice assistants influence user behavior.

A new tool analyzes and addresses cultural disparities across language versions, paving the way for more balanced information.

Johns Hopkins researchers show that large language models can generate realistic patient data for training AI models without compromising individual privacy.

An AI model created by Johns Hopkins computer scientists imagines in-depth scenarios based on a single image to make informed decisions about the world.

Bestowing machines with the ability to perceive the physical world as humans do has been a career-long mission of Alan Yuille, a pioneer in the field of computer vision.

Johns Hopkins researchers used betting strategies to help clarify AI models’ decision-making processes.

A recent innovation from Johns Hopkins researchers enables deeper insights into gene function and disease-linked mutations.

A new JHU targeted training approach can make up for a lack of natural spatial ability in robot teleoperation tasks.