An artificial intelligence blood testing technology developed and used by Johns Hopkins Kimmel Cancer Center researchers to successfully detect lung cancer in a 2021 study has now detected more than 80% of liver cancers in a new study of 724 people.
Researchers at Johns Hopkins Kimmel Cancer Center and its Bloomberg~Kimmel Institute for Cancer Immunotherapy successfully trained a machine learning algorithm to predict, in hindsight, which patients with melanoma would respond to treatment and which would not, in a small study.
Researchers at Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine have developed an automated system to calculate metabolic tumor volume in diffuse large B-cell lymphoma. These findings could make it easier to calculate tumor volume for clinical trials and possibly patient care.
MD Anderson Cancer Center and Exscientia plc formed a strategic collaboration to align the drug discovery and development expertise of MD Anderson with the patient-centric artificial intelligence capabilities of Exscientia in order to advance novel small-molecule oncology therapies.
Louisiana State University Health New Orleans, an NCI-NCORPS designated institution, and ConcertAI, LLC have formed a five-year partnership to improve the diversity of clinical trials and ensure broader clinical trial access throughout the Gulf South region.
Researchers at University of California San Diego School of Medicine have been selected to lead components of the NIH Common Fund’s Bridge to Artificial Intelligence program.
Indivumed GmbH and CELLphenomics GmbH have formed a partnership to create a unique platform for faster and more efficient discovery and validation of therapeutic targets.
C2i Genomics and Karkinos Healthcare have partnered to co-develop the minimal residual disease market in India.
Case Western Reserve University signed an exclusive license agreement with Picture Health to develop AI technologies to help predict, diagnose, and treat lung cancer.
Researchers from the Gwangju Institute of Science and Technology have developed a deep learning model that predicts unexpected drug-drug interactions (DDIs) based on their effects on gene expression.
