Here are the top stories covered by DocWire News recently regarding artificial intelligence (AI) in healthcare. In this edition, read about StethoMe’s smart-stethoscope used in at-home medicine, an AI system that created a flu vaccine on its own, a tool for detecting brain activity in clinically unresponsive patients, screening candidates for laser eye surgery, and more. Stay tuned for more emerging news regarding this innovative technology in the healthcare space.
StethoMe Smart Stethoscope Revolutionizing Remote Medicine
One of the most important aspects of the routine visit to the doctor’s office is the use of the stethoscope, known as auscultation. This technique allows the physician to listen to the lungs, heart, and other organs for unusual sounds that signify various conditions. Though many apps and telemedicine platforms have allowed patients to seek medical care in the comfort of their own homes, none have been able to replicate this essential in-person procedure. By recording sound and using AI, however, the unique StethoMe device has the power to add auscultation to remote healthcare.
Human Vaccine Created Solely by Artificial Intelligence
For the first time ever, a human drug has been created entirely by an AI system. This news comes from a team at Flinders University in Australia, who claims to have created an enhanced influenza vaccine using an AI program known Search Algorithm for Ligands (SAM). Though computers have been used to make drugs before, this was the first time it was done independently by an AI system.
Australian researchers have developed a new #vaccine believed to be the first in the world to be designed by #AI (via @BIAUS) https://t.co/zFoGS5Kpf9
— Exponential Medicine (@ExponentialMed) July 15, 2019
Finding Brain Activity in Clinically Unresponsive Patients
Researchers have recently used AI to detect the brain’s response to verbal commands in clinically unresponsive patients. These findings indicate that although these patients are not able to move in response to commands, they may still be consciously interpreting what is said. This phenomenon, in which there is a lag between processing thought into movement, is known as cognitive-motor dissociation. An article detailing this work was published on June 27 in the New England Journal of Medicine.
Identifying Laser Eye Surgery Candidates Using Machine Learning
Machine learning AI has recently been used to distinguish between patients who are fit for corneal refractive surgery and those who are likely to experience post-operative complications. The referral for this procedure often goes misdiagnosed, but by using AI, these researchers have potentially created an accurate screening tool for the surgery. Their work was published on June 20 in the journal npj Digital Medicine.
Adopting machine learning to automatically identify candidate patients for corneal refractive surgery https://t.co/ZQjtaK5RHv via @mydocwire
— Sergyl Lafont. (@SergylB) July 2, 2019
Computer Model Uses Artificial Intelligence to Support Cancer Therapy
Researchers have recently created a machine learning computer model that can simulate the metabolism of cancer cells. This team, from the Life Sciences Research Unit at the University of Luxembourg, used this technique to analyze the effects of various drugs on stopping cancer development. Their findings were covered in an open access article in the Lancet journal EBioMedicine.
Predicting the Likelihood of Death from Chest Images Years in Advance
Chest radiography, such as x-ray imaging, is essential in diagnosing many conditions and is the most common imaging test in healthcare. In 2013, there were 1,039 outpatient chest radiographs done per 1,000 US Medicare Part B members, highlighting the frequency of this procedure in older patients. These images typically come back with no indication of major disease, but even these “normal” radiographs can indicate subtle issues like enlarged heart or arterial stiffening.
Being that the physician does not often see what their patients’ outcomes are decades after reviewing these radiographs, it’s hard to say which subtleties of these images have long-term value. To address this discrepancy, researchers hypothesized that a deep learning AI model could be used to predict 12-year mortality from chest radiographs. Their findings were published on July 19 in JAMA Network Open.
Deep learning #AI from a single chest X-ray to predict long term mortality https://t.co/A6xZcxiG6i @JAMANetworkOpen by Michael Lu and colleagues pic.twitter.com/p6iYD30qHC
— Eric Topol (@EricTopol) July 19, 2019