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Found 174 results
  1. Event
    This Westminster Health Forum conference will examine the priorities and next steps for utilising AI-driven technologies within health and social care. Delegates will consider the opportunities for increased use, what is needed to tackle barriers to implementation, data protection, questions of ethics and bias, wider regulatory challenges, and priorities for research. It will be a timely opportunity to consider next steps for harnessing AI-based healthcare solutions to deliver streamlined and effective care following developments made during the pandemic - and in the context of the development of an AI Strategy for Health and Social Care. Overall, the agenda will bring out latest thinking on: priorities for the development of a national AI Strategy for Health and Social Care addressing the key ethical and legal issues in the development of AI-based health solutions key issues surrounding data security and sharing, priorities for ensuring patient anonymity, data confidentiality and providing transparency around data use the future for research and innovation in the development of AI-driven technologies priorities for workforce education and training around AI health solutions addressing barriers to the use of AI in healthcare, developing digital infrastructure across the health service, and improving the diversity of clinical research data. Register
  2. Content Article
    The theme for the 4th Learning from Excellence Community Event was “Being better, together”, reflecting LfE's aspiration to grow as individuals, and as part of a community, through focussing on what works. For this event, LfE partnered with the Civility Saves Lives (CSL) team, who promote the importance of kindness and civility at work and seek to help us to address the times this is lacking in a thoughtful and compassionate way, through their Calling it out with Compassion programme.
  3. News Article
    Researchers are to use artificial intelligence (AI) in the hope of reducing risk to pregnant black women. Loughborough University experts are to work with the Healthcare Safety Investigation Branch (HSIB) to identify patterns in its recent investigations. Research has suggested black women are more than four times more likely to die in pregnancy or childbirth than white women in the UK. The researchers plan to look at more than 600 of HSIB's recent investigations into adverse outcomes during pregnancy and birth. The research team will develop a machine learning system capable of identifying factors, based on a set of codes, that contribute to harm during pregnancy and birth experienced by black families. These include biological factors, such as obesity or birth history; social and economic factors such as language barriers and unemployment; and the quality of care and communication with the mother. It will look at how these elements interact with and influence each other, and help researchers design ways to improve the care of black mothers and babies. Dr Patrick Waterson, from the university, who is helping to lead the project, said: "Ultimately, we believe the outcomes from our research have the potential to transform the NHS's ability to reduce maternal harm amongst mothers from black ethnic groups." He added that in the longer term, the research could improve patient safety for all mothers. Read full story Source: BBC News, 17 November 2021
  4. News Article
    Artificial intelligence (AI) systems being developed to diagnose skin cancer run the risk of being less accurate for people with dark skin, research suggests. The potential of AI has led to developments in healthcare, with some studies suggesting image recognition technology based on machine learning algorithms can classify skin cancers as successfully as human experts. NHS trusts have begun exploring AI to help dermatologists triage patients with skin lesions. But researchers say more needs to be done to ensure the technology benefits all patients, after finding that few freely available image databases that could be used to develop or “train” AI systems for skin cancer diagnosis contain information on ethnicity or skin type. Those that do have very few images of people with dark skin. Dr David Wen, first author of the study from the University of Oxford, said: “You could have a situation where the regulatory authorities say that because this algorithm has only been trained on images in fair-skinned people, you’re only allowed to use it for fair-skinned individuals, and therefore that could lead to certain populations being excluded from algorithms that are approved for clinical use." “Alternatively, if the regulators are a bit more relaxed and say: ‘OK, you can use it [on all patients]’, the algorithms may not perform as accurately on populations who don’t have that many images involved in training.” That could bring other problems including risking avoidable surgery, missing treatable cancers and causing unnecessary anxiety, the team said. Read full story Source: The Guardian, 9 November 2021
  5. Content Article
    Artificial intelligence (AI) is increasingly being used in medicine to help with the diagnosis of diseases such as skin cancer. To be able to assist with this, AI needs to be ‘trained’ by looking at data and images from a large number of patients where the diagnosis has already been established, so an AI programme depends heavily upon the information it is trained on. This review, published in The Lancet Digital Health, looked at all freely accessible sets of data on skin lesions around the world.
  6. Content Article
    The Healthcare Safety Investigation Branch (HSIB) identified a patient safety risk caused by delays in diagnosing lung cancer. Lung cancer is the third most common cancer diagnosed in England, but accounts for the most deaths. Two-thirds of patients with lung cancer are diagnosed at an advanced stage of the disease when curative treatment is no longer possible, a fact which is reflected in some of the lowest five-year survival rates in Europe. Chest X-ray is the first test used to assess for lung cancer, but about 20% of lung cancers will be missed on X-rays. This results in delayed diagnosis that will potentially affect a patient’s prognosis. The HSIB investigation reviewed the experience of a patient who saw their GP multiple times and had three chest X-rays where the possible cancer was not identified. This resulted in an eight-month delay in diagnosis and potentially limited the patient’s treatment options.
  7. Content Article
    This new book by Professor Harold Thimbleby of Swansea University tells stories of widespread problems with digital healthcare and explores how they can be overcome. "The stories and their resolutions will empower patients, clinical staff and digital developers to help transform digital healthcare to make it safer and more effective."
  8. Content Article
    The Chartered Institute of Ergonomics & Human Factors (CIEHF) have published a new white paper intended to promote systems thinking among those who develop, regulate, procure, and use AI applications in healthcare, and to raise awareness of the role of people using or affected by AI.
  9. Content Article
    Researchers have developed an artificial intelligence (AI) tool for rapidly detecting COVID-19 in people arriving at a hospital’s emergency department. The tool can accurately rule out infection within an hour of a patient arriving at hospital, significantly faster than the PCR (polymerase chain reaction) test that has a turnaround time of typically 24 hours. 
  10. News Article
    New research has emerged that may be able to diagnose dementia after a single brain scan. Scientists have begun testing a new artificial intelligence system that could identify the condition and predict predict whether it will remain stable for many years, slowly deteriorate or need immediate treatment. Prof Zoe Kourtzi, of Cambridge University and a fellow of national centre for AI and data science The Alan Turing Institute, said "If we intervene early, the treatments can kick in early and slow down the progression of the disease and at the same time avoid more damage". Read full story. Source: BBC News, 10 August 2021
  11. Content Article
    Healthcare is becoming both increasingly data driven and automated. Authors of this blog, published by the London School of Economics, found that opportunities for patients to influence and inform these future technologies are often lacking, which in turn may heighten disillusionment and lack of trust in them. As such, they propose four priorities for new data driven technologies to ensure they are ethical, effective and equitable for diverse patient groups: Public voice Individual’s diversity Participatory co-design Open knowledge development and exchange. Read the blog in full via the link below.
  12. Content Article
    Artificial intelligence 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 physicians 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, as Howard et al. demonstrate in this study.
  13. 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
  14. 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.
  15. 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
  16. 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,
  17. Content Article
    Patient safety and digital experts have given their views on immediate digital priorities that could make a significant difference in the NHS.
  18. 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.
  19. 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
  20. 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.
  21. 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
  22. 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.
  23. 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.”
  24. 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.
  25. 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
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