Many patients use their inhalers and insulin pens wrong. Researchers have developed a system to reduce those numbers for some types of medications.
Artificial intelligence has unimaginable potential to revolutionize medicine. We cover the latest technology breakthroughs of machine learning and deep learning algorithms that process mindboggling amounts of data, spot even the smallest detail in medical images and help medical professionals in designing treatment plans.
Using AI and mobile digital microscopy, researchers hope to create screening tools that can detect precursors to cervical cancer in women in resource-limited settings.
Researchers have found that out of the more than 300 COVID-19 machine learning models are not suitable for detecting or diagnosing COVID-19 from standard medical imaging.
Researchers have succeeded in making an AI understand our subjective notions of what makes faces attractive.
Researchers at the Indian Institute of Science and SigTuple Technologies have developed a method to measure hemoglobin levels in small-volume blood samples.
AI is helping researchers decipher images from a new holographic microscopy technique needed to investigate a key process in cancer immunotherapy “live” as it takes place.
Deep learning-based system enables dermatologist-level identification of suspicious skin lesions from smartphone photos, allowing better screening.
A deep learning model that can predict how human genes and medicines will interact has identified at least 10 compounds that may hold promise as treatments for COVID-19.
A machine learning system learns on the job. By continuously adapting to new data inputs, this “liquid network” could aid decision-making in medical diagnosis.
Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
Using theoretical calculations, scientists showed that it would not be possible to control a superintelligent AI.
Physicians who follow AI advice may be considered less liable for medical malpractice than is commonly thought, according to a new study of potential jury candidates in the U.S.
Scientists have developed a machine learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.