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Given AI models’ ubiquity and influence, how and why should we trust their decisions? Can we be certain their predictions are free of biases or errors? Johns Hopkins AI experts discuss.

The International Conference on Information Processing in Computer-Assisted Interventions is a top-tier venue for disseminating innovative, peer-reviewed research in computer-assisted surgery and minimally invasive interventions.

Co-created by a Johns Hopkins professor and CS alum, the interactive science visual compares the weight of the natural with the man-made.

Johns Hopkins researchers are using Loop-X Mobile Imaging Robot by Brainlab to forge the future of the intelligent operating room.

Unberath is one of two recipients of the Johns Hopkins University Data Science and AI Institute’s inaugural Data Science and AI Trusted Dataset Awards.

The John C. Malone Assistant Professorships were endowed through the generosity of John C. Malone, Engr ’64, ’69, to support outstanding Whiting School faculty members within the Malone Center for Engineering in Healthcare.

Qian Wang was selected to attend the conference as part of the RSAC Security Scholar program.

The Hopkins-led team demonstrated that an AI model trained solely on synthetic tumor data works as well as models trained on real tumors.

Ben Langmead took home the Best Paper Award while Steven Salzberg was one of five distinguished keynote speakers at the annual International Conference on Research in Computational Molecular Biology.

Awardees will be granted discretionary funds to support current or developing research and academic endeavors.

A Johns Hopkins-led team found that chatbots reinforce our biases, providing insight into how AI could widen the public divide on controversial issues.

Outstanding students and faculty were honored at the annual Computer Science Department Awards Ceremony.