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Found 162 results
  1. News Article
    Artificial intelligence (AI) tools and deep learning models are a powerful tool in cancer treatment. They can be used to analyse digital images of tumour biopsy samples, helping doctors quickly classify the type of cancer, predict prognosis and guide a course of treatment for the patient. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions. A new study led by researchers from the University of Chicago shows that deep learning models trained on large sets of cancer genetic and tissue histology data can easily identify the institution that submitted the images. The models, which use machine learning methods to "teach" themselves how to recognise certain cancer signatures, end up using the submitting site as a shortcut to predicting outcomes for the patient, lumping them together with other patients from the same location instead of relying on the biology of individual patients. This in turn may lead to bias and missed opportunities for treatment in patients from racial or ethnic minority groups who may be more likely to be represented in certain medical centres and already struggle with access to care. "We identified a glaring hole in the in the current methodology for deep learning model development which makes certain regions and patient populations more susceptible to be included in inaccurate algorithmic predictions," said Alexander Pearson, one of the authors of the study. Read full story Source: Digital Health News, 22 July 2021
  2. Content Article
    Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear. This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. It found that the ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.
  3. News Article
    Google has unveiled a tool that uses artificial intelligence to help spot skin, hair and nail conditions, based on images uploaded by patients. A trial of the "dermatology assist tool", unveiled at the tech giant's annual developer conference, Google IO, should launch later this year, it said. The app has been awarded a CE mark for use as a medical tool in Europe. A cancer expert said AI advances could enable doctors to provide more tailored treatment to patients. The AI can recognise 288 skin conditions but is not designed to be a substitute for medical diagnosis and treatment, the firm said. Read full story Source: BBC News, 18 May 2021
  4. News Article
    Virtual wards, at-home antibiotic kits and using artificial intelligence in GP surgeries are among new initiatives to be trialled as part £160m funding to tackle waiting lists in the NHS. NHS England announced the funding to aid in the health service’s recovery after the pandemic, after figures last month revealed the number of people waiting to begin hospital treatment in England had risen to a new record. A total of 4.7 million people were waiting to start treatment at the end of February - the highest figure since records began in August 2007. But NHS England said indicators suggest operations and other elective activity were at four-fifths of pre-pandemic levels in April, which is "well ahead" of the 70% threshold set out in official guidance. It said it is working to speed up the health service's recovery by trialling new ways of working in 12 areas and five specialist children's hospitals. The so-called "elective accelerators" will each get some of the £160m as well as extra support for new ways to increase the number of elective operations, NHS England said. Tens of thousands of patients in the trial areas will be part of initiatives including a high-volume cataract service, one-stop testing facilities and pop-up clinics to allow patients to be seen and discharged closer to home. Other trials over the next three months include virtual wards and home assessments, 3D eye scanners, at-home antibiotic kits, "pre-hab" for patients ahead of surgery, artificial intelligence in GP surgeries and so-called "Super Saturday" clinics, bringing multi-disciplinary teams together at the weekend to offer more specialist appointments. Read full story Source: The Independent,
  5. Content Article
    Patient safety and digital experts have given their views on immediate digital priorities that could make a significant difference in the NHS.
  6. Content Article
    In his newsletter today (The Top 10 Dangers of Digital Health), the medical futurist, Bertalan Meskó, raises some very topical questions about the dangers of digital health. As a huge advocate of the benefits of digital health, I am aware of most of these but tend to downplay the negative aspects as I generally believe that in this domain the good outweighs the bad. However, as I was reading his article, I realised that it was written very much from the perspective of a clinician and, to some extent, a healthcare organisation too. The patient perspective was included but not from a patient safety angle. Many of the issues that he raises do have significant patient safety issues associated with them which I’d like to share in this blog.
  7. Event
    until
    AI mainly refers to doctors and hospitals analysing vast data sets of potentially life-saving information through AI algorithms. However, in primary care it has been getting an increasingly important role in standardising triaging, creating intelligent patient pathways and supporting prioritisation and decision making with urgency indications and differential diagnoses. In this webinar, we will provide an overview of applications of AI in improving patient flow and patient transfer within healthcare settings. Learn how organisations are achieving real results with AI supported triaging. Discover how to leverage Intelligent patient pathway management can increase capacity. See how you can build a strong data-driven organisation, while improving staff morale and the patient experience. Register
  8. Content Article
    The use of artificial intelligence in healthcare is often touted as a technology which can transform how tasks are carried out across the NHS. Rachel Dunscombe, CEO of the NHS digital academy and director for Tektology, and Jane Rendall, UK managing director for Sectra, examine what needs to happen to make sure AI is used safely in healthcare in this article for Digital Health.
  9. Community Post
    Subject: Looking for Clinical Champions (Patient Safety Managers, Risk Managers, Nurses, Frontline clinical staff) to join AI startup Hello colleagues, I am Yesh. I am the founder and CEO of Scalpel. <www.scalpel.ai> We are on a mission to make surgery safer and more efficient with ZERO preventable incidents across the globe. We are building an AI (artificially intelligent) assistant for surgical teams so that they can perform safer and more efficient operations. (I know AI is vaguely used everywhere these days, to be very specific, we use a sensor fusion approach and deploy Computer Vision, Natural Language Processing and Data Analytics in the operating room to address preventable patient safety incidents in surgery.) We have been working for multiple NHS trusts including Leeds, Birmingham and Glasgow for the past two years. For a successful adoption of our technology into the wider healthcare ecosystem, we are looking for champion clinicians who have a deeper understanding of the pitfalls in the current surgical safety protocols, innovation process in healthcare and would like to make a true difference with cutting edge technology. You will be part of a collaborative and growing team of engineers and data scientists based in our central London office. This role is an opportunity for you to collaborate in making a difference in billions of lives that lack access to safe surgery. Please contact me for further details. Thank you Yesh yesh@scalpel.ai
  10. Content Article
    Failure to attend scheduled hospital appointments disrupts clinical management and consumes resource estimated at £1 billion annually in the UK NHS alone. Accurate stratification of absence risk can maximise the yield of preventative interventions. The wide multiplicity of potential causes, and the poor performance of systems based on simple, linear, low-dimensional models, suggests complex predictive models of attendance are needed. In this paper, Nelson et al. quantify the effect of using complex, non-linear, high-dimensional models enabled by machine learning.
  11. News Article
    Talking Medicines, a social intelligence company for the pharmaceutical industry, has secured £1.1 million funding deal to scale up its AI-based platform for measuring patient sentiment. Tern, an investment company specialising in the Internet of Things (“IoT”), is the lead investor in a syndicated funding round alongside The Scottish Investment Bank, Scottish Enterprise’s investment arm. Led by CEO Jo Halliday alongside co-founders Dr Elizabeth Fairley and Dr Scott Crae, Talking Medicines will use the funds to support the launch and roll-out of a new AI data platform, which will translate what patients are saying into intelligence by providing a global patient confidence score by medicine. As part of these plans, the business intends to immediately recruit 9 new employees to the NLP data tech team. Formed in 2013 to create new ways of capturing the voice of the patient, the Glasgow-based firm uses a combination of AI, machine learning and Natural Language Processing (NLP) tech tools to capture and analyse the conversations and behaviours of patients at home, with the aim of transforming big pharma’s understanding of patient sentiment. Through mapping the patient voice from social media and connected devices to regulated medicine information, it is able to build data points to determine trends and patterns of patient sentiment across medicines. The round brings the total raised by the firm to £2.5m, including three previous seed funding rounds with previous investors including impact investor SIS Ventures and the Scottish Investment Bank. Talking Medicines CEO Halliday, said: “This investment will scale our team and the development of our AI, ML, NLP tech tools to translate what patients are saying into actionable pharma grade intelligence through our global patient confidence score by medicine.”
  12. Content Article
    One of the areas where Human Factors is getting more traction is within the healthcare sector. It is still a slow burner though with lots more work to be done, and this is getting more urgent as new technologies are available to make procedures and processes better and potentially support more effective patient outcomes. Dr Mark Sujan has taken this challenge head on by launching the Artificial Intelligence and Digital Health Special Interest Group with the CIEHF. In this podcast, we find out more about Mark and his motivations, as well as what his intentions for the Special Interest Group are.
  13. Event
    until
    NCRI Virtual Showcase will feature a number of topical sessions, panel discussions and proffered paper presentations covering the latest discoveries across: Big data and AI Prevention and early detection Immunology and immunotherapy Living with and beyond cancer Cancer research and COVID-19 Further information and registration
  14. Content Article
    As trusts consider clearing the waiting list, there is an absence of objective approaches to prioritisation. There are 40 million variations of operative type and the NHS elective waiting list may reach more than 10 million. A coronavirus second wave may cause further delays and expansion of the waiting list. This blog from hub topic lead Richard Jones describes a proven approach to prioritising the waiting list built around individualised risk-adjustment for each patient and evolved from the core POSSUM methodology that is widely used for individual risk assessment pre-operatively.
  15. Content Article
    The COVID-19 crisis has created a watershed moment for the NHS, demanding a reappraisal of how essential services are delivered to the public. Even prior to COVID-19, the NHS recognised a pressing need to rethink healthcare using user-centred design principles, based on populations, not organisations. With the advent of the pandemic that pressing need has become an operational imperative. Digital capability has been and will continue to be a key part of transformation, but will only work when aligned with reforms in other key enablers such as financial flow, workforce planning and regulation. Many industries have already made the shift to enabling collaboration and innovation through more agile models of delivery by embracing technologies like artificial intelligence (AI), internet of things (IoT) and/or flexible and secure forms of (multi) cloud storage. Health, on the other hand, until now has introduced new technologies with the objective of improving existing pathways and service delivery models. There is now an opportunity to reimagine healthcare, driving true transformation enabled by digital capabilities.
  16. Content Article
    Although millions of patients with cancer around the world face delays in diagnosis and treatment because of the diversion of resources during the COVID-19 pandemic, there is a growing expectation that telemedicine may play a central role in easing the backlog. This Lancet Digital Health article explores how telemedicine will be key as healthcare systems move forward in tackling the backlog in not only cancer treatment but also diagnosis, and how augmented intelligence (AI) could be used to help to optimise its use.
  17. Content Article
    AI health chatbots around the world have been racing to add coronavirus detection into algorithms or put up helpful information to demonstrate they are part of the response to coronavirus (COVID-19). But to be honest, it’s pointless. A symptom checker can’t diagnose you with COVID-19. That can only be done through testing. The symptoms are too close to cold and flu. However, Prof Dr. Maureen Baker, Chief Medical Officer at Your.MD and former Chair of the UK’s Royal College of General Practitioners, has been involved at the highest level of pandemic preparation planning in the UK for decades and she is clear that AI chatbots, like Your.MD, can play a vital role in reducing the number of people who unnecessarily seek medical treatment and the deaths of individuals who are endangered by symptoms unrelated to COVID-19. So, if AI health chatbots can’t reliably detect COVID-19 and should only advise you to stay at home, what else can they do? “They can work in tandem with governments and health services to stop the worried well not at risk from the virus from seeking treatment, and also support people to self-care where that is appropriate,” says Prof Baker. She thinks that with collaboration, there is enormous potential for chatbots to act as reliable companions providing guidance and tracking symptoms.
  18. Content Article
    The COVID-19 pandemic is sweeping across the length and breadth of the UK. As a result, NHS England has issued guidelines for effective triaging of urgent cancer 'two-week wait' referrals. The intention of this guideline is to minimise the disruption to cancer services. In order to fully understand the implications of this manual triage approach, this article, Data-Drive Triage Automation – YouDiagnose’s fight against COVID-19, will first explain the triage process during normal circumstances, and then highlight the additional impacts due to the coronavirus emergency. Finishing with a suggested solution (from YouDiagnose) to improve the efficiency of the triaging process and save lives during the pandemic. 
  19. News Article
    Technology and healthcare companies are racing to roll out new tools to test for and eventually treat the coronavirus epidemic spreading around the world. But one sector that is holding back are the makers of artificial-intelligence-enabled diagnostic tools, increasingly championed by companies, healthcare systems and governments as a substitute for routine doctor-office visits. In theory, such tools, sometimes called “symptom checkers” or healthcare bots,sound like an obvious short-term fix: they could be used to help assess whether someone has Covid-19, the illness caused by the novel coronavirus, while keeping infected people away from crowded doctor’s offices or emergency rooms where they might spread it. These tools vary in sophistication. Some use a relatively simple process, like a decision tree, to provide online advice for basic health issues. Other services say they use more advanced technology, like algorithms based on machine learning, that can diagnose problems more precisely. But some digital-health companies that make such tools say they are wary of updating their algorithms to incorporate questions about the new coronavirus strain. Their hesitancy highlights both how little is known about the spread of Covid-19 and the broader limitations of healthcare technologies marketed as AI in the face of novel, fast-spreading illnesses. Some companies say they don’t have enough data about the new coronavirus to plug into their existing products. London-based symptom-checking app Your.MD Ltd. recently added a “coronavirus checker” button that leads to a series of questions about symptoms. But it is based on a simple decision tree. The company said it won’t update the more sophisticated technology underpinning its main system, which is based on machine learning. “We made a decision not to do it through the AI because we haven’t got the underlying science,” said Maureen Baker, Chief Medical Officer for Your.MD. She said it could take 6 to 12 months before sufficient peer-reviewed scientific literature becomes available to help inform the redesign of algorithms used in today’s more advanced symptom checkers. Read full story Source: The Wall Street Journal, 29 February 2020
  20. News Article
    In his latest blog post, Matthew Gould, CEO of NHSX, has reiterated the potential AI has to reduce the burden on the NHS by improving patient outcomes and increasing productivity. However, he said there are gaps in the rules that govern the use of AI and a lack of clarity on both standards and roles. These gaps mean there is a risk of using AI that is unsafe and that NHS organisations will delay employing AI until all the regulatory gaps have been filled. Gould says, “The benefits will be huge if we can find the sweet spot” that allows trust to be maintained whilst creating the freedom for innovation but warns that we are not in that position yet. At the end of January, the CEOs and heads of 12 regulators and associated organisations met to work through these issues and discuss what was required to ensure innovation-friendly processes and regulations are put in place. They agreed there needs to be a clarity of role for these organisations, including the MHRA being responsible for regulating the safety of AI systems; the Health Research Agency (HRA) for overseeing the research to generate evidence; NICE for assessing whether new AI solutions should be deployed; and the CQC to ensure providers are following best practice. Read the full blog Source: Techradar, 13 February 2020
  21. News Article
    London doctors are using artificial intelligence to predict which patients with chest pains are at greatest risk of death. A trial at Barts Heart Centre, in Smithfield, and the Royal Free Hospital, in Hampstead, found that poor blood flow was a “strong predictor” of heart attack, stroke and heart failure. Doctors used computer programmes to analyse images of the heart from more than 1,000 patients and cross-referenced the scans with their health over the next two years. The computers were “taught” to search for indicators of future “adverse cardiovascular outcomes” and are now used in a real-time basis to help doctors identify who is most at risk. Read full story Source: Evening Standard, 15 February 2020
  22. Content Article
    There is increasing use of algorithms in the healthcare and criminal justice systems, and corresponding increased concern with their ethical use. But perhaps a more basic issue is whether we should believe what we hear about them and what the algorithm tells us.  Large numbers of algorithms of varying complexity are being developed within the healthcare and the criminal justice system, and include, for example, the UK HART (Harm Assessment Risk Tool) system for assessing recidivism risk, which is based on a machine-learning technique known as a random forest. But the reliability and fairness of such algorithms for policing are being strongly contested: apart from the debate about facial recognition on predictive policing algorithms says that ”their use puts our rights at risk.”
  23. News Article
    In a keynote speech at the Healthtech Alliance on Tuesday, Secretary of State for Health and Social Care, Matt Hancock, stressed how important adopting technology in healthcare is and why he believes that it is vital for the NHS to move into the digital era. “Today I want to set out the future for technology in the NHS and why the techno-pessimists are wrong. Because for any organisation to be the best it possibly can be, rejecting the best possible technology is a mistake.” Listing examples from endless paperwork to old systems resulting in wasted blood samples, Hancock highlights why in order to retain staff and see a thriving healthcare, embracing technology must be a priority. He also announced a £140m Artificial Intelligence (AI) competition to speed up testing and delivery of potential NHS tools. The competition will cover all stages of the product cycle, to proof of concept to real-world testing to initial adoption in the NHS. Examples of AI use currently being trialled were set out in the speech, including using AI to read mammograms, predict and prevent the risk of missed appointments and AI-assisted pathways for same-day chest X-ray triage. Tackling the issue of scalability, Hancock said, “Too many good ideas in the NHS never make it past the pilot stage. We need a culture that rewards and incentivises adoption as well as invention.” Read full speech
  24. News Article
    Artificial intelligence can diagnose brain tumours more accurately than a pathologist in a tenth of the time, a study has shown. The machine-learning technology was marginally more accurate than a traditional diagnosis made by a pathologist, by just 1%, but the results were available in less than 2 minutes and 30 seconds, compared with 20 to 30 minutes by a pathologist. The study, published in Nature Medicine, demonstrates the speed and accuracy of AI diagnosis for brain surgery, allowing surgeons to detect and remove otherwise undetectable tumour tissue. Daniel Orringer, an Associate Professor of Neurosurgery at New York University's Grossman School of Medicine and a senior author, said: “As surgeons, we’re limited to acting on what we can see; this technology allows us to see what would otherwise be invisible to improve speed and accuracy in the [operating theatre] and reduce the risk of misdiagnosis." “With this imaging technology, cancer operations are safer and more effective than ever before.” Read full story Source: The Independent, 6 January 2020
  25. News Article
    Artificial intelligence is more accurate than doctors in diagnosing breast cancer from mammograms, a study in the journal Nature suggests. An international team, including researchers from Google Health and Imperial College London, designed and trained a computer model on X-ray images from nearly 29,000 women. The algorithm outperformed six radiologists in reading mammograms. AI was still as good as two doctors working together. Unlike humans, AI is tireless. Experts say it could improve detection. Sara Hiom, director of cancer intelligence and early diagnosis at Cancer Research UK, told the BBC: "This is promising early research which suggests that in future it may be possible to make screening more accurate and efficient, which means less waiting and worrying for patients, and better outcomes." Read full story Source: BBC News, 2 January 2020
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