Jump to content

Search the hub

Showing results for tags 'AI'.

More search options

  • Search By Tags

    Start to type the tag you want to use, then select from the list.

  • Search By Author

Content Type


Forums

  • All
    • Commissioning, service provision and innovation in health and care
    • Coronavirus (COVID-19)
    • Culture
    • Improving patient safety
    • Investigations, risk management and legal issues
    • Leadership for patient safety
    • Organisations linked to patient safety (UK and beyond)
    • Patient engagement
    • Patient safety in health and care
    • Patient Safety Learning
    • Professionalising patient safety
    • Research, data and insight
    • Miscellaneous

Categories

  • Commissioning, service provision and innovation in health and care
    • Commissioning and funding patient safety
    • Digital health and care service provision
    • Health records and plans
    • Innovation programmes in health and care
    • Climate change/sustainability
  • Coronavirus (COVID-19)
    • Blogs
    • Data, research and statistics
    • Frontline insights during the pandemic
    • Good practice and useful resources
    • Guidance
    • Mental health
    • Exit strategies
    • Patient recovery
    • Questions around Government governance
  • Culture
    • Bullying and fear
    • Good practice
    • Occupational health and safety
    • Safety culture programmes
    • Second victim
    • Speak Up Guardians
    • Staff safety
    • Whistle blowing
  • Improving patient safety
    • Clinical governance and audits
    • Design for safety
    • Disasters averted/near misses
    • Equipment and facilities
    • Error traps
    • Health inequalities
    • Human factors (improving human performance in care delivery)
    • Improving systems of care
    • Implementation of improvements
    • International development and humanitarian
    • Safety stories
    • Stories from the front line
    • Workforce and resources
  • Investigations, risk management and legal issues
    • Investigations and complaints
    • Risk management and legal issues
  • Leadership for patient safety
    • Business case for patient safety
    • Boards
    • Clinical leadership
    • Exec teams
    • Inquiries
    • International reports
    • National/Governmental
    • Patient Safety Commissioner
    • Quality and safety reports
    • Techniques
    • Other
  • Organisations linked to patient safety (UK and beyond)
    • Government and ALB direction and guidance
    • International patient safety
    • Regulators and their regulations
  • Patient engagement
    • Consent and privacy
    • Harmed care patient pathways/post-incident pathways
    • How to engage for patient safety
    • Keeping patients safe
    • Patient-centred care
    • Patient Safety Partners
    • Patient stories
  • Patient safety in health and care
    • Care settings
    • Conditions
    • Diagnosis
    • High risk areas
    • Learning disabilities
    • Medication
    • Mental health
    • Men's health
    • Patient management
    • Social care
    • Transitions of care
    • Women's health
  • Patient Safety Learning
    • Patient Safety Learning campaigns
    • Patient Safety Learning documents
    • Patient Safety Standards
    • 2-minute Tuesdays
    • Patient Safety Learning Annual Conference 2019
    • Patient Safety Learning Annual Conference 2018
    • Patient Safety Learning Awards 2019
    • Patient Safety Learning Interviews
    • Patient Safety Learning webinars
  • Professionalising patient safety
    • Accreditation for patient safety
    • Competency framework
    • Medical students
    • Patient safety standards
    • Training & education
  • Research, data and insight
    • Data and insight
    • Research
  • Miscellaneous

News

  • News

Find results in...

Find results that contain...


Date Created

  • Start
    End

Last updated

  • Start
    End

Filter by number of...

Joined

  • Start

    End

Group

First name

Last name

Country

Join a private group (if appropriate)

About me

Organisation

Role

Found 158 results
  1. Content Article
    Gender bias in healthcare is a well-recognised issue. From diagnosis to drug development and treatment, the modern healthcare system has been shown to advantage men over women. Responsibly designed artificial intelligence (AI) and machine learning algorithms have the potential to overcome gender bias in medicine. However, if machine learning methods are implemented without careful thought and consideration they can lead to the perpetuation and even accentuation of existing biases. How can we develop technology in a way that prevents rather than perpetuates bias? This blog from Babylon highlights 4 key principles that can help.
  2. Content Article
    Medication errors can occur at any point in the system for prescribing, dispensing and administering drugs in the NHS – and can often be the result of human errors creeping in as burned out staff misread or miscalculate the amount needed. This article in the Health Services Journal examines how closed loop medication management systems can improve patient safety by ensuring patients are prescribed the right dosage of the right medications. The author speaks to Islam Elkonaissi, former lead pharmacist for cancer services in Cambridge, about the importance of well-planned implementation and bridging the gap between IT specialists and healthcare workers to make sure that potential for communication errors is minimised. They also discuss the value of the huge amounts of data AI systems can collect, which in turn make the systems more precise and accurate.
  3. Content Article
    A key part of healthcare digital transformation is the development and adoption of artificial intelligence technologies. This article, published in BMJ Health & Care Informatics, considers how human factors and ergonomic principles can be applied to the use of artificial intelligence in healthcare.
  4. News Article
    A Scottish hospital has become the first in the UK and one of the first in the world to pilot using artificial intelligence (AI) in its cervical cancer screening programme. University Hospital Monklands has increased capacity by around 25% and improved analysis turn-around times with the measure, which experts said could “revolutionise” the screening process. The system, from medical technology company Hologic, creates digital images of cervical smear slides from samples that have tested positive for Human Papilloma Virus (HPV). These are then reviewed using an advanced algorithm, which quickly assesses the cells in the sample and highlights the most relevant to medical experts, saving them time in identifying and analysing abnormalities. “Preliminary results from the pilot are promising, as the team at University Hospital Monklands has increased capacity by around 25 per cent in the slide assessment and improved analysis turn-around times, as well as allowing screeners to dedicate more time to training on the latest technologies and dealing with difficult-to-diagnose cases,” says Allan Wilson, consultant biomedical scientist at NHS Lanarkshire who is leading the pilot. "Through AI and digital diagnostics, we have the potential to improve outcomes for women not only in Scotland, but around the world.” Samantha Dixon, chief executive of Jo’s Cervical Cancer Trust, welcomed the pilot. “Catching cervical cell changes means they can be treated to prevent them from developing into cervical cancer,” she said. Read full story Source: The Scotsman, 4 March 2022
  5. Content Article
    Skin cancer is one of the most common cancers worldwide, with one in five people in the US expected to receive a skin cancer diagnosis during their lifetime. Detecting and treating skin cancers early is key to improving survival rates. This blog for The Medical Futurist looks at the emergence of skin-checking algorithms and how they will assist dermatologists in swift diagnosis. It reviews research into the effectiveness of algorithms in detecting cancer, and examines the issues of regulation, accessibility and the accuracy of smartphone apps.
  6. News Article
    Medical students aided by an AI tutor outperformed peers taught remotely by human experts in a complicated surgical training procedure, new research reports. The Neurosurgical Simulation and Artificial Intelligence Learning Centre in Montreal, Canada, randomly assigned 70 students feedback and assistance from either a sophisticated AI system, a remote expert human instructor, or neither, while they removed virtual brain tumours using a neurosurgical simulator. The AI system, called the Virtual Operative Assistant (VOA), delivered personalised feedback to its students via a machine learning algorithm to teach them safe surgical techniques. Human instructors observed the students over a live feed and gave instructions based on their performance. The students tutored by the AI system learned surgical skills 2.6 times faster and performed 36 per cent better than those advised by human experts, without experiencing the heightened stress the researchers had anticipated. Using AI training models to tutor students could be an effective way to improve their skills and patient safety while reducing the burdens placed on human instructors, the study, published in the Journal of the American Medical Association, found. “Artificially intelligent tutors like the VOA may become a valuable tool in the training of the next generation of neurosurgeons,” said Dr Rolando Del Maestro, the study’s senior author. Read full story Source: iNews, 22 February 2022
  7. Event
    This webinar chaired by Dr Jennifer Dixon, Chief Executive of The Health Foundation and featuring Dr Tim Ferris, NHS England’s Director of Transformation, will explore the next steps for service transformation at scale. Against the backdrop of the recent Wade-Gery review, the data strategy, the forthcoming Goldacre review and AI strategy, the new tech fund to support elective recovery, and a renewed focus on delivering the tech ambitions outlined in the Long Term Plan, how can these be linked to support service transformation better in practice? What will be different this time? Register
  8. News Article
    A cervical cancer patient has been treated with the aid of artificial intelligence (AI) for the first time in the UK. Emma McCormick, 44, was treated at the St Luke's Cancer Centre in Guildford, Surrey. The Royal Surrey NHS Foundation Trust treated Ms McCormick, who is from West Sussex, using adaptive radiotherapy. The AI technology uses daily CT scans to target the specific areas that need radiotherapy. This helps to avoid damage to healthy tissue and limit side-effects, the hospital said. Patients are given treatments lasting between 20 and 25 minutes, although Ms McCormick's was slightly longer as she was the first patient, a hospital spokesman said. Ms McCormick received five AI-guided treatments per week for five weeks before having a further two weeks of brachytherapy. She said: "If it works for me, and they get information from me, it can help somebody else. It definitely worked and did what it was meant to do and so hopefully that helps others." Dr Alex Stewart, who treated Ms McCormick, said one of the benefits of the treatment was that it allowed for more precision, meaning there were fewer side-effects for the patients. Read full story Source: BBC News, 21 January 2022
  9. Content Article
    Reports from the G7 working groups on AI governance and interoperability setting out how the G7 are implementing their commitments on digital health.
  10. 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
  11. 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.
  12. 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
  13. 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
  14. 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.
  15. 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.
  16. 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."
  17. 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.
  18. 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. 
  19. 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
  20. 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.
  21. 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.
  22. 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
  23. 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.
  24. 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
  25. 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,
×
×
  • Create New...