The healthcare industry has seen a sea of change in the last couple of years: from being autonomous to becoming automated, clinician-centric to patient-centric, disjointed to coordinated, reactive to proactive, retrospective to predictive and siloed to aware – if there is one industry which has undergone a complete transformation, it is undoubtedly healthcare. In this modern era, enhancing patient satisfaction requires business administrators to balance resources with demand, optimize workflows, mitigate waste, contain costs, and facilitate collaboration across the healthcare organization. The growing physician and nurse shortage and the increasingly competitive healthcare industry has made it challenging to attract and retain qualified personnel. With doctor shortage expected to reach 120,000 by the end of 2030, handling the increasingly elderly population has become extremely difficult, if not impossible. This is why optimizing workforce management for high-value care has become a top priority for the business administration at any hospital.

Artificial intelligence and machine learning will play an even bigger role in healthcare in 2020 than they did in 2019, helping medical professionals with everything from oncology screenings to note-taking. On top of actual deployments, increased investment activity is also expected this year, and with deeper deployments of AI and ML technology, a broader base of test cases will be available to collect valuable best practices information. As AI is implemented more widely in real-world clinical practice, there will be more academic reports on the clinical benefits that have arisen from the real-world use, said Pete Durlach, senior vice president for healthcare strategy and new business development at Nuance. "With healthy clinical evidence, we'll see AI become more mainstream in various clinical settings, creating a positive feedback loop of more evidence-based research and use in the field," he explained. "Soon, it will be hard to imagine a doctor's visit, or a hospital stay that doesn't incorporate AI in numerous ways."

The findings, published in The Lancet Oncology in December, suggest AI systems can be trained to detect and grade cancer in prostate needle biopsy samples with an accuracy rate equal to that of international prostate pathology experts. Furthermore, the study noted that the use of AI technology could help reduce the workload of oncologists by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores, as well as providing a second opinion. "An AI system with expert-level grading performance might aid in standardizing grading, and provide pathology expertise in parts of the world where it does not exist," the report noted. The system was developed by the team behind Stockholm3 and OncoWatch, two projects supported by EIT Health, a network of top health innovators backed by the EU. A team at Karolinksa Institutet launched Stockholm3, a blood-based prostate cancer diagnostic test, in 2017, which is currently used in clinical practice in Sweden, Norway, Finland and Denmark.

Massachusetts General Hospital is buying into deep learning artificial intelligence, and it will use Nvidia's new DGX-1 deep-learning supercomputer. Nvidia is partnering with the MGH Clinical Data Science Center, which wants to advance health care with AI to improve the detection, diagnosis, treatment, and management of diseases. "Deep learning is revolutionizing a wide range of scientific fields," said Jen-Hsun Huang, CEO of Nvidia, at the company's GPUTech event in San Jose, California, today. "There could be no more important application of this new capability than improving patient care.

Massachusetts General Hospital is buying into deep learning artificial intelligence, and it will use Nvidia's new DGX-1 deep-learning supercomputer that was announced today. Nvidia is partnering with the MGH Clinical Data Science Center, which wants to advance health care with AI to improve the detection, diagnosis, treatment, and management of diseases. "Deep learning is revolutionizing a wide range of scientific fields," said Jen-Hsun Huang, CEO of Nvidia, at the company's GPUTech event in San Jose, California, today. "There could be no more important application of this new capability than improving patient care. Massachusetts General Hospital runs the largest hospital-based research program in the United States, and is the top-ranked hospital on this year's U.S. News and World Report's "Best Hospitals" list. The center will train a deep neural network using Mass General's vast stores of phenotypic, genetics, and imaging data. The hospital has a database containing some 10 billion medical images. To do this, it will use the Nvidia DGX-1 -- a supercomputer designed for AI applications. Using AI, physicians can compare a patient's symptoms, tests, and history with insight from a vast population of other patients. Initially, the MGH Clinical Data Science Center will focus on the fields of radiology and pathology -- which are particularly rich in images and data -- and then expand into genomics and electronic health records. "We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation," said Keith J. Dreyer, vice chairman of Radiology at Mass General and executive director of the center, in a statement. "Guided by precision healthcare, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations.