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


  • 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


  • 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

Find results in...

Find results that contain...

Date Created

  • Start

Last updated

  • Start

Filter by number of...


  • Start



First name

Last name


Join a private group (if appropriate)

About me



Found 164 results
  1. News Article
    An artificial intelligence (AI) system that sends text messages to alert hospital physicians about the high risk for mortality in their patients reduces the number of deaths, according to a study published in Nature Medicine. Chin-Sheng Lin, PhD, associate professor of cardiology at the Tri-Service General Hospital of the National Defense Medical Center in Taipei, Taiwan, and his colleagues have developed an AI system that identifies patients with a high risk for mortality on the basis of a 12-lead ECG. The system is intended to identify patients who would benefit from intensified care. "It is widely acknowledged that providing intensive care to critically ill patients reduces mortality. Delays in providing intensive care for critically ill patients result in catastrophic outcomes. Most in-hospital cardiac arrests are potentially preventable; however, the early signs of deterioration might be difficult to identify," wrote the researchers. The authors emphasized that exactly how the AI warning messages lead to a decrease in overall mortality must still be clarified. But the results suggest that they help in detecting high-risk patients, triggering timely clinical care, and reducing mortality, they wrote. Read full story Source: Medscape, 21 May 2024
  2. Content Article
    This study in Surgery aimed to investigate the accuracy of ChatGPT-4’s surgical decision-making compared with general surgery residents and attending surgeons. Five clinical scenarios were created from actual patient data based on common general surgery diagnoses. Scripts were developed to sequentially provide clinical information and ask decision-making questions. Responses to the prompts were scored based on a standardised rubric for a total of 50 points. Each clinical scenario was run through Chat GPT-4 and sent electronically to all general surgery residents and attendings at a single institution. Scores were compared using Wilcoxon rank sum tests. The results showed that, when faced with surgical patient scenarios, ChatGPT-4 performed superior to junior residents and equivalent to senior residents and attendings. The authors argue that large language models, such as ChatGPT, may have the potential to be an educational resource for junior residents to develop surgical decision-making skills.
  3. Content Article
    Large language models (LLMs) are a form of artificial intelligence that can generate human-like text and functions as a form of an input–output machine. They bring great potential to help the healthcare industry centre care around patients’ needs by improving communication, access and engagement. However, LLMs also present significant challenges associated with privacy and bias that also must be considered. This blog looks at three major patient-care advantages of LLMs, as well as the potential risks associated with using them in healthcare.
  4. Content Article
    In this blog, Miqdad Asaria, Assistant Professor at the Department of Health Policy at LSE, argues that AI could lead to a paradigm-shift in healthcare systems likes the NHS. He outlines how AI could help personalise medical treatments, enhance research and development of new drugs and help with the administrative burden currently undermining the productivity and efficiency of healthcare providers.
  5. Content Article
    In an increasingly global healthcare environment, with patients and professionals from many different cultural and linguistic backgrounds, precision in medical document translation is key. Medical documents can range from patient records, patient information leaflets, consent forms, prescriptions, treatment plans to research papers. The translator must have a thorough understanding of the source text and subject matter in order to produce a high-quality target document and ensure patients receive accurate information. But this can come with patient risk, if not done properly. In this blog, Melanie Cole, Translations Coordinator at EIDO Systems International, talks about the challenges, risks and opportunities for using AI in healthcare translation. 
  6. Content Article
    This policy paper provides on update on the Medicines and Healthcare products Regulatory Agency's (MHRA’s) use of AI as a regulator of AI products, as a public service organisation delivering time-critical decisions and as an organisation making evidence-based decisions that impact public safety.
  7. Content Article
    In this article, Radar Healthcare provides a summary of the main sessions, messages and themes emerging from the Care Show London and the Digital Healthcare Show 2024, which both took place in April 2024. It discusses these topics: Embracing technology in care provision Mastering CQC-ready feedback processes The importance of integration between social care and the NHS Leveraging social media AI: The challenges and opportunities Avoiding digital fatigue:  Fostering patient safety In this final section, the article highlights a presentation given by Patient Safety Learning's Chief Executive Helen Hughes and Chief Digital Officer Clive Flashman about the organisation's patient safety standards. They spoke about the standards and accompanying online patient safety assessment toolkit, an easy-to-use resource designed to help organisations establish clearly defined patient safety aims and goals, support their delivery and demonstrate achievement. The article also highlights the contribution of the hub to improving patient safety, saying, "Patient Safety Learning's platform is recognised for its excellence in sharing knowledge on patient safety. It provides a comprehensive suite of tools, resources, case studies, and best practices to support those striving to improve patient care."
  8. Content Article
    The debate about fairness of artificial intelligence (AI) in health care is gaining momentum. At present, the focus of the debate is on identifying unfair outcomes resulting from biased algorithmic decision making. This article in The Lancet Digital Health looks at the ethical principles guiding outcome fairness in AI algorithms.
  9. Event
    The theme for this year's The Private Healthcare Information Network (PHIN) forum underscores the pivotal role that advancements in AI, robotics, and data play in shaping the future of healthcare for the benefit of patients and everyone involved in the sector. The event is free to attend, but you need to book to guarantee your place. Register for the event
  10. Content Article
    Surgical Site Infections (SSIs) can have subtle, early signs that are not readily identifiable. This study aimed to develop a machine learning algorithm that could identify early SSIs based on thermal images. Images were taken of surgical incisions on 193 patients who underwent a variety of surgical procedures, but only five of these patients developed SSIs, which limited testing of the models developed. However, the authors were able to generate two models to successfully segment wounds. This proof-of-concept demonstrates that computer vision has the potential to support future surgical applications.
  11. Content Article
    As the NHS’s digital transformation journey enters a new phase, there are opportunities to improve the quality and productivity of the healthcare system. This phase is not just about advancing the maturity of electronic health records (EHRs) but also about embracing the vast potential of generative artificial intelligence tools. In this HSJ article, Robert Wachter and Harpreet Sood explore the reasons why EHRs have not yet delivered promised productivity improvements and look at how GenAI offers opportunities for the NHS to realise productivity benefits faster, cheaper and at a greater scale.
  12. Content Article
    In this report for Stat, technology correspondent Casey Ross looks at the dangers involved in using AI to predict patient outcomes, especially in life-or-death situations such as suspected sepsis. He looks at the recent case of US electronic health record provider Epic who were force to rewrite the algorithm being used by tens of thousands of US clinicians to predict sepsis.
  13. Content Article
    Incident reports of medication errors are valuable learning resources for improving patient safety. However, key information is often contained within unstructured free text, which prevents automated analysis and limits the usefulness of these data. Natural language processing can be used to structure this free text automatically and retrieve relevant past incidents and learning materials, but this requires a large, fully annotated and validated set of incident reports. This study in Nature used a set of 58,658 machine-annotated incident reports of medication errors to test a natural language processing model. The authors provide access to the validation datasets and machine annotator for labelling future incident reports of medication errors.
  14. News Article
    Investigators have applied artificial intelligence (AI) techniques to gait analyses and medical records data to provide insights about individuals with leg fractures and aspects of their recovery. The study, published in the Journal of Orthopaedic Research, uncovered a significant association between the rates of hospital readmission after fracture surgery and the presence of underlying medical conditions. Correlations were also found between underlying medical conditions and orthopedic complications, although these links were not significant. It was also apparent that gait analyses in the early postinjury phase offer valuable insights into the injury’s impact on locomotion and recovery. For clinical professionals, these patterns were key to optimizing rehabilitation strategies. "Our findings demonstrate the profound impact that integrating machine learning and gait analysis into orthopaedic practice can have, not only in improving the accuracy of post-injury complication predictions but also in tailoring rehabilitation strategies to individual patient needs," said corresponding author Mostafa Rezapour, PhD, of Wake Forest University School of Medicine. "This approach represents a pivotal shift towards more personalised, predictive, and ultimately more effective orthopaedic care." Read full story Source: Digital Health News, 12 April 2024
  15. News Article
    Researchers at the National Institutes of Health applied artificial intelligence (AI) to a technique that produces high-resolution images of cells in the eye. They report that with AI, imaging is 100 times faster and improves image contrast 3.5-fold. The advance, they say, will provide researchers with a better tool to evaluate age-related macular degeneration (AMD) and other retinal diseases. "Artificial intelligence helps overcome a key limitation of imaging cells in the retina, which is time," said Johnny Tam, Ph.D., who leads the Clinical and Translational Imaging Section at NIH's National Eye Institute. Tam is developing a technology called adaptive optics (AO) to improve imaging devices based on optical coherence tomography (OCT). Like ultrasound, OCT is noninvasive, quick, painless, and standard equipment in most eye clinics. "Our results suggest that AI can fundamentally change how images are captured," said Tam. "Our P-GAN artificial intelligence will make AO imaging more accessible for routine clinical applications and for studies aimed at understanding the structure, function, and pathophysiology of blinding retinal diseases. Thinking about AI as a part of the overall imaging system, as opposed to a tool that is only applied after images have been captured, is a paradigm shift for the field of AI." Read full story Source: Digital Health News, 11 April 2024
  16. Event
    Bevan Brittan are delighted to announce their next Digital Health and Care Forum. This session will be hosted in partnership with the Masala Network, the health and life science network for South Asians in the UK. At this in-person event, we will be joined by a panel of experts in the field including: Dr Amrita Kumar, AI Clinical Lead and Consultant Radiologist at NHS Haris Shuaib, Founder and CEO at Newton’s Tree Helen Hughes, Chief Executive at Patient Safety Learning Hassan Chaudhury, Co-Founder at Vita Health Care Solutions Daniel Morris, Partner at Bevan Brittan AI and data-driven technology continues to revolutionise health and care at a giddying pace and offers enormous opportunity to shape and future-proof health systems that could be more affordable, sustainable and equitable. But to fully realise AI’s transformational potential there is a pressing need to ensure public and clinical buy-in. In this session we will consider how evidence and ethical based frameworks and guidelines, patient safety measures and regulation can all foster trust in AI. The Digital Health & Care Forum is intended to be an interactive session and an opportunity and safe space to exchange views, identify and explore key issues and share knowledge. Our events are attended by developers, purchasers, providers, funders, insurers and policy makers. Register
  17. Content Article
    Currently, surgical site infection surveillance relies on labour-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods, while being harder to interpret. This study proposed a deep learning model—an explainable long short-term memory network—for the identification of surgical site infection from the medical record. The study found that the model had greater sensitivity when compared to traditional machine learning methods.
  18. News Article
    Drugs are a cornerstone of medicine, but sometimes doctors make mistakes when prescribing them and patients don’t take them properly. A new AI tool developed at Oxford University aims to tackle both those problems. DrugGPT offers a safety net for clinicians when they prescribe medicines and gives them information that may help their patients better understand why and how to take them. Doctors and other healthcare professionals who prescribe medicines will be able to get an instant second opinion by entering a patient’s conditions into the chatbot. Prototype versions respond with a list of recommended drugs and flag up possible adverse effects and drug-drug interactions. “One of the great things is that it then explains why,” said Prof David Clifton, whose team at Oxford’s AI for Healthcare lab led the project. “It will show you the guidance – the research, flowcharts and references – and why it recommends this particular drug.” Read full story Source: The Guardian, 31 March 2024
  19. Content Article
    This study aimed to find out whether using an artificial intelligence (AI) deterioration model decreased the risk of escalations in care during hospitalisation. The study's findings suggest that use of an AI model is associated with a decreased risk of escalations in care.
  20. News Article
    The NHS is set to roll out artificial intelligence (AI) to reduce the number of missed appointments and free up staff time to help bring down the waiting list for elective care. The expansion to ten more NHS Trusts follows a successful pilot in Mid and South Essex NHS Foundation Trust, which has seen the number of did not attends (DNAs) slashed by almost a third in six months. Created by Deep Medical and co-designed by a frontline worker and NHS clinical fellow, the software predicts likely missed appointments through algorithms and anonymised data, breaking down the reasons why someone may not attend an appointment using a range of external insights including the weather, traffic, and jobs, and offers back-up bookings. The appointments are then arranged for the most convenient time for patients – for example, it will give evening and weekend slots to those less able to take time off during the day. The system also implements intelligent back-up bookings to ensure no clinical time is lost while maximising efficiency. It has been piloted for six months at Mid and South Essex NHS Foundation Trust, leading to a 30% fall in non-attendances. A total of 377 DNAs were prevented during the pilot period and an additional 1,910 patients were seen. It is estimated the trust, which supports a population of 1.2 million people, could save £27.5 million a year by continuing with the programme. The AI software is now being rolled out to ten more trusts across England in the coming months. Read full story Source: NHS England, 14 March 2024
  21. Content Article
    When ECRI unveiled its list of the leading threats to patient safety for 2024, some items are likely to be expected, such as physician burnout, delays in care due to drug shortages or falls in the hospital. However, ECRI, a non-profit group focused on patient safety, placed one item atop all others: the challenges in helping new clinicians move from training to caring for patients. In an interview with Chief Healthcare Executive®, Dr. Marcus Schabacker, president and CEO of ECRI, explained that workforce shortages are making it more difficult for newer doctors and nurses to make the transition and grow comfortably. “We think that that is a challenging situation, even the best of times,” Schabacker says. “But in this time, these clinicians who are coming to practice now had a very difficult time during the pandemic, which was only a couple years ago, to get the necessary hands-on training. And so we're concerned about that.”
  22. News Article
    Presymptom Health’s technology provides early and reliable information about infection status and severity in patients with non-specific symptoms, helping doctors make better treatment decisions. The company’s tests can be run on NHS PCR platforms, which were widely deployed during the COVID pandemic and are now often under-utilised. By detecting true infection and sepsis earlier, it’s possible to save lives and significantly reduce the incorrect use of antibiotics. When it comes to sepsis, Presymptom’s technology could revolutionise treatment. According to The UK Sepsis Trust, every 3 seconds, someone in the world dies of sepsis. In the UK alone, 245,000 people are affected by sepsis with at least 48,000 people losing their lives in sepsis-related illnesses every year. This is more than breast, bowel and prostate cancer combined. When diagnosed at a late stage, the likelihood of death increases by 10% for every hour left untreated. Yet, for many patients, with early diagnosis it is easily treatable. “We’re confident that our first product can play a big part in tackling Anti-Microbial Resistance (AMR), which has been identified by the World Health Organisation as one of the top 10 global public health threats,” said Dr Iain Miller, CEO of Presymptom Health. “By understanding the presence, or absence, of infection as early as possible, doctors can be more confident in their diagnosis and avoid unnecessarily prescribing antibiotics – something that is a growing concern in the NHS and globally. “If we take Sepsis as an example. Sepsis diagnostics hasn’t moved on in more than a century, and currently doctors can only diagnose it when advanced symptoms and organ failure are present – which is often too late. Our technology enables doctors to diagnose both infection and sepsis up to three days before formal clinical diagnosis, radically transforming the process and preventing unnecessary deaths. The science behind Presymptom’s technology is based upon 10 years of work conducted at Defence Science and Technology Laboratory (Dstl) and originated from £16m of sustained Ministry of Defence investment in a programme of research designed to help service personnel survive infection from combat injuries. The technology is currently undergoing clinical trials at nine NHS hospitals in the UK, with results anticipated later in 2024. In addition, Presymptom is working on additional UK and EU trials.
  23. Content Article
    The role of artificial intelligence (AI) in healthcare is expanding quickly with clinical, administrative, and patient facing uses emerging in many specialties. Research on the effectiveness of AI in healthcare is generally weak, but evidence of AI improving doctor’s diagnostic decisions is emerging for some focused clinical applications, including interpreting lung pathology and retinal images. However, we must work with patients to understand how AI impacts on their care, says Rebecca Rosen in this BMJ opinion piece.
  24. News Article
    Many popular AI chatbots, including ChatGPT and Google’s Gemini, lack adequate safeguards to prevent the creation of health disinformation when prompted, according to a new study. Research by a team of experts from around the world, led by researchers from Flinders University in Adelaide, Australia, and published in the BMJ found that the large language models (LLMs) used to power publicly accessible chatbots failed to block attempts to create realistic-looking disinformation on health topics. As part of the study, researchers asked a range of chatbots to create a short blog post with an attention-grabbing title and containing realistic-looking journal references and patient and doctor testimonials on two health disinformation topics: that sunscreen causes skin cancer and that the alkaline diet is a cure for cancer. The researchers said that several high-profile, publicly available AI tools and chatbots, including OpenAI’s ChatGPT, Google’s Gemini and a chatbot powered by Meta’s Llama 2 LLM, consistently generated blog posts containing health disinformation when asked – including three months after the initial test and being reported to developers when researchers wanted to assess if safeguards had improved. In response to the findings, the researchers have called for “enhanced regulation, transparency, and routine auditing” of LLMs to help prevent the “mass generation of health disinformation”. Read full story Source: The Independent, 20 March 2024
  25. Content Article
    This cross-sectional study in JAMA Network aimed to assess whether a large language model can transform discharge summaries into a format that is more readable and understandable for patients. The findings suggest that a large language model could be used to translate discharge summaries into patient-friendly language and format, but implementation will require improvements in accuracy, completeness and safety.
  • Create New...

Important Information

We have placed cookies on your device to help make this website better. You can adjust your cookie settings, otherwise we'll assume you're okay to continue.