Let's start with the one minute version: I was part of the EF12 London cohort in 2019, where I met my co-founder. A privacy-preserving medical-data marketplace and AI platform built around federated deep learning. The purpose of the platform would have been to allow data scientists to train deep learning models on highly sensitive healthcare data without that data ever leaving the hospitals. At the same time, thanks to a novel data monetization strategy and marketplace component, hospitals would have been empowered to make money from the data they are generating. We received pre-seed funding, valued at $1 million. Then the race for demo day began with frantic product building and non-stop business development.
It is official: GNU Solidario, the Spanish NGO behind GNU Health (GH) and Khadas Technologies have signed the "GNU Health Alliance of Academic and Research Institutions", to research and deliver Artificial Intelligence to the world of medicine. GNU Health covers genomics, medical genetics, Dx imaging and social medicine, areas where definitely we can use the power of AI for better diagnostics, personalized treatments, decision support, disease prevention and health promotion. What is truly revolutionary is that we will be using affordable single board computers, like the Khadas VIM3, where they could work alone or in the context of the GNU Health Federation, doing massive virtual parallel computing. Citizens, health professionals and institutions don't have to spend millions to access the latest in technology. In GNU Health, this has always been our philosophy.
In May 2017, National Health Service (NHS) hospitals in England and Scotland were virtually shut down for several days because of the global WannaCry cyberattack. The attack resulted in the cancellation of thousands of appointments and operations and some NHS services had to turn away noncritical emergencies. Up to 70,000 devices, including computers, MRI scanners, blood-storage refrigerators, and operating room equipment may have been affected. And in 2016, the Hollywood Presbyterian Medical Center in Los Angeles paid $17,000 in bitcoin to a hacker to unlock data that had been encrypted in an attack. Hospital staff struggled to deal with the loss of email and access to patient data.
Our focus on AI and Data at University of Birmingham is two-fold, covering education to bridge sector skills gaps with Degree Apprenticeships and MSc programmes, alongside well established research communities promoting new ways of working and new insights into data and AI. We know the tech world changes rapidly. We are collaborating with industry sectors such as IT and computer science, engineering and professional services by developing innovative courses whilst also promoting the latest insights from research directly to business. Researchers at University of Birmingham and experts from industry are working on various projects for the UKRI AI for Services network initiatives. We are a partner in The Alan Turing Institute, the UK's national institute for data science and artificial intelligence.
Despite recent evidence that physician extenders can streamline radiology workflow and reduce turnaround times, radiologists should rely more on artificial intelligence (AI) for assistance than non-physician providers (NPP), a group of industry experts has said. In an editorial published in the Journal of the American College of Radiology, a team of experts, led by Daniel Ortiz, M.D., with Summit Radiology in Georgia, pointed to the several benefits of AI – not only do the tools save money and streamline workflow, but they will not encroach on a radiologist's responsibilities. For these reasons, they said, radiologists should forego giving physician extenders – the nurse practitioners, physician assistants, and other providers who take on some of a provider's duties – full practice authority. "Although labor costs have been reduced and radiologists can focus more on complex imaging studies and interventional procedures, there are unintended consequences of non-physician practitioners in practice that could diminish physician's role as healthcare providers," the group wrote. "Therefore, we encourage radiologists to consider an alternative to non-physician practitioners in radiology: the incorporation of rapidly evolving artificial intelligence algorithms into daily practice." Their concern was spawned by a recently passed Georgia law that allows advanced practice registered nurses to order CR, MRI, and other imaging exams under certain circumstances.
While AI and machine learning have the potential for transforming healthcare, the technology has inherent biases that could negatively impact patient care, senior FDA officials and Philips' head of global software standards said at the meeting. Bakul Patel, director of FDA's new Digital Health Center of Excellence, acknowledged significant challenges to AI/ML adoption including bias and the lack of large, high-quality and well-curated datasets. "There are some constraints because of just location or the amount of information available and the cleanliness of the data might drive inherent bias. We don't want to set up a system and we would not want to figure out after the product is out in the market that it is missing a certain type of population or demographic or other other aspects that we would have accidentally not realized," Patel said. Pat Baird, Philips' head of global software standards, warned without proper context there will be "improper use" of AI/ML-based devices that provide "incorrect conclusions" provided as part of clinical decision support.
MY 85-year-old uncle was hospitalised due to ageing issues at a private hospital in Kolkata, India. In the intensive care unit, he contracted Covid-19 from another patient whose infection the hospital was not even aware of. It took the hospital days to figure out that many of its ICU patients, doctors and other professionals were already infected. The pandemic shows us the inequality of healthcare access. This collective global experience will invariably lead to demands of massive upscaling of healthcare.
Over the last decade, data and analytics have grown to be more than just a quantitative support function. Many organizations have traditionally used data to win customers and market share. However they are now also leveraging data to re-design future products based on evolving customer needs and macro trends. While significant progress has been made in the field of machine learning, as well as artificial intelligence –there is one critical element to making this all work: having the right data. Business decisions that are built using flawed data can cause an organization significant revenue loss, increased expenses, compliance issues, possible legal issues and even more severe ramifications.
Artificial intelligence (AI) is already delivering on making aspects of health care more efficient. Over time it will likely be essential to supporting clinical and other applications that result in more insightful and effective care and operations. AI has multiple use cases throughout health plan, pharmacy benefit manager (PBM), and health system enterprises today, and with more interoperable and secure data, it is likely to be a critical engine behind analytics, insights, and the decision-making process. Enterprises that lean into adoption are likely to gain immediate returns through cost reduction and gain competitive advantage over the longer term as they use AI to transform their products and services to better engage with consumers. Get the Deloitte Insights app.
In 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource. The refrain has been repeated ever since. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. According to IDG's State of the CIO 2020 report, 37 percent of IT leaders say that data analytics will drive the most IT investment at their organization this year. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.