Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit. Machine learning (ML), artificial intelligence (AI), and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. The potential uses include improving diagnostic accuracy,1 more reliably predicting prognosis,2 targeting treatments,3 and increasing the operational efficiency of health systems.4 Examples of potentially disruptive technology with early promise include image based diagnostic applications of ML/AI, which have shown the most early clinical promise (eg, deep learning based algorithms improving accuracy in diagnosing retinal pathology compared with that of specialist physicians5), or natural language processing used as a tool to extract information from structured and unstructured (that is, free) text embedded in electronic health records.2 Although we are only just …
Artificial Intelligence (AI) platforms which can provide faster diagnosis and treatments for clinicians are to be developed as part of a new partnership. The London Medical Imaging and AI Centre for Value-Based Healthcare and digital transformation consultancy Answer have teamed up to help pave the way for AI-enabled hospitals. The platforms will be developed to support clinicians with faster diagnosis and treatments, personalised therapies, and effective screening across a range of conditions and procedures. Federated learning – a model seeking to address the problem of data governance and privacy by training algorithms collaboratively without exchanging the data itself – will also be used to help address long standing privacy issues. Beverley Bryant, chief digital information officer for Guy's and St Thomas' NHS Foundation Trust and King's College Hospital NHS Foundation Trust, and senior responsible owner for the London Medical Imaging and AI Centre for Value-Based Healthcare, said: "This is a unique programme to implement AI at scale within the NHS. "Our partnership with Answer is another key step toward deploying this open-source technology in clinical environments.
Over the past decade, hospitals and other health care providers have put massive amounts of time and energy into adopting electronic health care records, turning hastily scribbled doctors' notes into durable sources of information. But collecting these data is less than half the battle. It can take even more time and effort to turn these records into actual insights -- ones that use the learnings of the past to inform future decisions. Cardea, a software system built by researchers and software engineers at MIT's Data to AI Lab (DAI Lab), is built to help with that. By shepherding hospital data through an ever-increasing set of machine learning models, the system could assist hospitals in planning for events as large as global pandemics and as small as no-show appointments.
Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."
Microsoft, on an accelerated growth push, is buying speech recognition company Nuance in a deal worth about $16 billion. The acquisition will get Microsoft deeper into hospitals and the health care industry through Nuance's widely used medical dictation and transcription tools. Microsoft will pay $56 per share cash. The companies value the transaction including debt at $19.7 billion. Shares of Burlington, Mass.-based Nuance surged more than 16% in Monday trading.
When Microsoft CEO Satya Nadella spoke to investors Monday about his company's plan to acquire speech-recognition specialist Nuance for $16 billion, he emphasized the importance of artificial intelligence in health care. Nuance's software listens to doctor-patient conversations and transcribes speech into organized digitized medical notes. This helps explain the hefty price tag, even as voice recognition has become commoditized and now comes packaged with every smartphone and laptop. But Microsoft may also see much broader potential for Nuance's technology. Gregg Pessin, an analyst with Gartner, says the deal gives Microsoft "an entry point into the health care industry, and a huge customer base already running this stuff."
Microsoft Corp.'s $16 billion deal for Nuance Communications Inc. is the latest sign that the next battleground for technology giants will be in healthcare, an industry whose need to embrace data and software was underscored by the pandemic. The acquisition will help Microsoft tap into Nuance's big business selling its software to healthcare systems, according to analysts and healthcare executives. Speech-recognition software like that developed by Nuance is emerging as an important new opportunity in medicine as doctors seek to speed up documentation of patient work with dictation rather than getting bogged down taking notes, executives said. "This coming together is about empowering healthcare," Satya Nadella, Microsoft's chief executive, said in an investor call. "It's now very clear that healthcare organizations that accelerate their digital investments can improve patient outcomes and reduce cost at scale."
Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you'll get a sense of how AI could transform patient care and diagnoses. In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines. You must complete all four courses and the capstone to earn the certificate.
Charlottesville, VA, USA, March 31, 2021--Unbound Medicine, a leader in knowledge management solutions for healthcare, today announced a major upgrade to their end-to-end digital publishing platform. To enhance clinical decision support capabilities for professional societies and healthcare institutions, Unbound developed Unbound Intelligence (UBI)‒exclusive artificial intelligence and machine learning tools to help clinicians keep up to date with current research, as well as discover and fill knowledge gaps. Unbound Intelligence quickly analyzes large volumes of data and recommends options for next steps in patient management. While clinicians answer questions or research areas of interest on the Unbound Platform, UBI instantly filters through available resources, including the most up-to-date primary literature, to suggest closely related topics and relevant, recently published journal articles. This allows clinicians to quickly expand their reach and discover evidence-based guidance that may have otherwise gone unnoticed.
A team of University of Illinois researchers estimated the mortality costs associated with air pollution in the U.S. by developing and applying a novel machine learning-based method to estimate the life-years lost and cost associated with air pollution exposure. Scholars from the Gies College of Business at Illinois studied the causal effects of acute fine particulate matter exposure on mortality, health care use and medical costs among older Americans through Medicare data and a unique way of measuring air pollution via changes in local wind direction. The researchers--Tatyana Deryugina, Nolan Miller, David Molitor and Julian Reif--calculated that the reduction in particulate matter experienced between 1999-2013 resulted in elderly mortality reductions worth $24 billion annually by the end of that period. Garth Heutel of Georgia State University and the National Bureau of Economic Research was a co-author of the paper. "Our goal with this paper was to quantify the costs of air pollution on mortality in a particularly vulnerable population: the elderly," said Deryugina, a professor of finance who studies the health effects and distributional impact of air pollution.