The global population is ageing at an exponential rate. By 2050, the number of individuals aged 60 and over is expected to reach 2 billion.
Around the world, health and social systems need to overcome significant challenges to ensure they can provide for the changing demographics.
But medical professionals, clinicians and scientists are already being placed under increasing pressure to meet these demands. Currently, nearly half of the countries in the world have less than one physician per 1,000 people, a third of the threshold value needed to deliver quality healthcare.
Plus, as healthcare data goes digital, the amount of information medical providers collect and refer to is growing. By 2020, it is estimated that 2,314 exabytes of data will be generated yearly.
Finding innovative solutions has never been of greater importance. By utilizing AI, medical professionals can spend more time focussing where their expertise is needed most.
From the Lab to the Point Of Care
Bringing a drug to market takes, on average of 13 years and $2.6 billion. The rate of success for a drug going through clinical trials is only 12 percent.
AI is showing the potential to be a faster, more efficient way to find and develop new drugs.
For example, MELLODDY—a new drug-discovery consortium—aims to give pharmaceutical partners the ability to leverage the world’s largest collaborative drug compound dataset for AI training, without sacrificing data privacy.
From research to treatment, AI is transforming healthcare. For medical professionals, it can change the way they work, enable more accurate diagnoses, and improve efficiency
By enabling pharmaceutical companies to learn from each other’s findings without providing traditional competitors direct access to proprietary datasets, the consortium aims to improve the predictive performance of AI-based drug discovery.
With smarter models comes speedier and cheaper drug development.
But it is not only drug development where AI is playing a crucial role.
The London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare project aims to disrupt twelve clinical pathways in oncology, cardiology and neurology as well as improving diagnoses and patient care in the NHS.
As part of this, NVIDIA and King’s College London are collaborating to bring artificial intelligence in medical imaging to the point of care. In contrast to traditional medical testing, which involves sending scans for further analysis by specialists, point of care testing allows the results gained from X-rays, CT scans or MRI to be delivered immediately at the time of the patient-doctor interaction. The research will accelerate the discovery of critical data strategies, targeted AI problems and speed up deployment in clinics.
More Efficient and Accurate Diagnoses
In medical emergencies, a quick diagnosis can be a matter of life or death. AI can help those at the front line take better-informed decisions, faster.
The Denmark-based startup, Corti, built an AI tool to provide immediate feedback and guidance to emergency call responders. The speech-recognition software, Corti AI, can detect cardiac arrest within 50 seconds, which is more than 10 seconds faster than dispatchers unaided by AI—and every second counts. If cardiac arrest treatment is delayed by more than 10 minutes, a victim’s chance of survival is less than 5 percent.
AI is also assisting in situations where limited medical equipment or expertise is available.
Ultrasound enables the accurate, efficient and non-invasive diagnosis of a host of ailments, including appendicitis, heart abnormalities and many urological and gynecological conditions. But most emergency responders and medical professionals either aren’t trained or aren’t equipped to use the technology.
DESKi, a member of the NVIDIA Inception Program, our virtual accelerator for deep learning startups, has developed a system that combines deep learning algorithms and cutting-edge handheld ultrasound devices to deliver the expertise of cardiac health specialists. Using a wealth of training data from leading cardiology units and trained on NVIDIA DGX Station, the AI workstation for data science teams, DESKi’s series of neural networks can determine whether or not the ultrasound probe is in the correct position for acquiring accurate and insightful views of the heart. The company is also training its algorithms to automatically measure the left ventricle ejection fraction, which can help diagnose heart failure.
The Future of Cancer Treatment
Radiation therapy for cancer patients is a complex workflow that includes modeling the patient, contouring the target and organs at risk, simulating the treatment, planning and delivering the treatment.
One of the most time-consuming tasks in this process is protecting the healthy organs at risk that surround a patient’s tumor and need to be spared from excessive radiation dose. Traditionally, radiation oncologists contour the tumor target volume and organs at risk, deciding how much radiation should be used to treat tumors without damaging neighboring normal tissue.
To help oncologists develop radiation treatment plans faster, Siemens Healthineers is using an NVIDIA GPU-based supercomputing infrastructure to develop AI software that enables precision radiation therapy. Trained on over 4.5 million images using Siemens Healthineers’ Sherlock supercomputer, the AI model saves radiation-oncologist time and eases organs-at-risk contouring tasks.
From research to treatment, AI is transforming healthcare. For medical professionals, it can change the way they work, enable more accurate diagnoses, and improve efficiency. For patients, healthcare innovations lessen suffering, improve care, and save lives.