By: Jaimie Patterson

  1. Home
  2. News
Illustration of human lungs affected by coronavirus.

AI can now detect COVID-19 in lung ultrasound images

An automated detection tool developed by Johns Hopkins researchers could help ER doctors diagnose patients quickly and accurately.

Headshot of Stephanie Hicks over Malone Hall.

Stephanie Hicks invited to NAM Emerging Leaders Forum

The invitation-only forum is an annual assembly of outstanding early- and mid-career professionals with exceptional professional promise, achievement and leadership in biomedical science, health, health care, and related fields.

Images of the (b) monopolar electrosurgical instrument; (c) vacuum grasping instrument; (d) dual-camera vision system, sample holder, grounding pad, smoke evacuation tube, linear motion stage; and (e) simulated clinical setting featuring a porcine tongue specimen stretched using retraction sutures. The close views during the (f) surface incision and (g) deep margin dissection for a pseudotumor on a porcine tongue tissue.

Robotic surgeon precisely removes cancerous tumors

The Autonomous System for Tumor Resection, designed by a team of Johns Hopkins researchers, can remove tumors from the tongue with accuracy rivaling—or even potentially exceeding—that of human surgeons.

Several pill containers and loose pills lay on a pink tabletop.

New method aims to optimize HIV treatments, improve quality of life

A Johns Hopkins team develops a way to personalize antiretroviral therapy to reduce side effects.

Diagram of an eye receiving photoacoustic retinal stimulation via an epiretinal implant. Red arrows represent a laser pulse hitting the implant, which generates blue acoustic waves depicted as wavy lines that stimulate the underlying retinal cells.

New nanomaterial for retinal implants could someday help restore sight for millions

Emad Boctor and Seth Billings's approach converts light into sound, activating damaged eye cells.

A close-up photo of a doctor taking notes on a clip board.

In others’ words: Using large language models to accurately analyze doctors’ notes and improve the reliability of real-world AI applications

Johns Hopkins and Columbia University computer scientists teamed up to combat the inaccurate correlations that artificial intelligence and machine learning models learn from text data.