In recent years, the number of machine learning (ML) technologies gaining regulatory approval for healthcare has increased significantly allowing them to be placed on the market. However, the regulatory frameworks applied to them were originally devised for traditional software, which has largely rule-based behaviour, compared to the data-driven and learnt behaviour of ML. As the frameworks are in the process of reformation, there is a need to proactively assure the safety of ML to prevent patient safety being compromised. The Assurance of Machine Learning for use in Autonomous Systems (AMLAS) methodology was developed by the Assuring Autonomy International Programme based on well-established concepts in system safety. This review has appraised the methodology by consulting ML manufacturers to understand if it converges or diverges from their current safety assurance practices, whether there are gaps and limitations in its structure and if it is fit for purpose when applied to the healthcare domain. Through this work we offer the view that there is clear utility for AMLAS as a safety assurance methodology when applied to healthcare machine learning technologies, although development of healthcare specific supplementary guidance would benefit those implementing the methodology.

The following is sponsored content. Achieving LFPSE (Learning from Patient Safety Events) compliance is more than just meeting targets – the real driver is transforming patient safety by enabling continuous improvement, says Mark Fewster, chief product officer with Radar Healthcare. The way that health care workers report on patient safety events is changing – and the deadline for making it happen is looming. By March 2023, healthcare organisations in England should have transitioned from the current NRLS (National Reporting and Learning System) and be LFPSE (Learning from Patient Safety Events) compliant. This is more than a change in initials – the new system aims to transform how patient safety events are recorded across the country.

PocDoc has secured a CE mark for its AI driven digital reader of lateral flow tests. The cloud-based system allows any smartphone or tablet to become a digital reader of lateral flow tests and has been recorded as having over 98% accuracy. The COVID-19 pandemic highlighted the value of using lateral flow testing at scale and PocDoc's platform is a cost-effective solution to digitise and verify the test results, store the data, and integrate it back into healthcare systems with full end-to-end traceability so it can be analysed and acted upon in real-time. PocDoc's platform and accompanying smartphone app will allow any healthcare organisation to roll out lateral flow testing for any disease or marker at scale. This technology has huge applications in the developing world where the nearest pathology lab could be thousands of miles away from localised disease outbreaks.

Patient satisfaction is a top priority for many hospitals and healthcare organisations. With machine learning and (AI) patient data can become invaluable, providing insights into where improvement in the patient journey is needed. Machine learning systems provide an opportunity for hospitals to improve overall health outcomes, as patient satisfaction is strongly associated with greater compliance and increased treatment adherence, according to researchers. AI can also provide more personalised and convenient healthcare experiences. Chatbots used by healthcare organisations can also boost patient satisfaction.

The airline industry and healthcare are not two things you would usually put together but Jon Payne, technology strategist and innovator at InterSystems, argues they might be able to learn from each other. The healthcare industry has notoriously lagged behind in its investment in and adoption of information technology and Artificial Intelligence (AI) is no exception. However, the opportunities – and need – to leverage AI for innovation, process improvement, patient and physician satisfaction, and patient outcomes improvement have never been greater. The Covid-19 pandemic has already created a significant backlog of patients requiring medical appointments. And with GP surgeries in the UK now being urged to resume face-to-face appointments as soon as possible, having the most efficient booking system has never been more critical to ensuring wait times don't skyrocket – and GPs don't burn out.

Our April blog is from Dr. Tracy O'Regan. Tracy is a diagnostic radiographer who works at the Society & College of Radiographers (SCoR) as officer for clinical imaging and research. She sits on the steering committee for UK Research & Innovation (UKRI) Science & Technology Facilities Council (STFC) Cancer Diagnosis Network, she is a member of NHSx AI Imaging Advisory Board, and she provides officer support for a SCoR AI & Emerging Technologies Working Party who are currently consulting on a guidance document with recommendations and priorities for AI for UK professionals. In 2011 Nilsson wrote a book that explored 50 years of the development of Artificial Intelligence (AI) (1). Nilsson described AI winters and a series of false dawns; each progressed the path to our current stage of AI with the promise of machine learning, neural networks and deep learning. Despite that development, in the main, clinical imaging and radiotherapy professionals are still discussing AI as if it is a new fashion or perhaps even the emperor's new clothes.

Globally, healthcare organisations have accelerated adoption of artificial intelligence (AI), with the ones still implementing frameworks planning to go live within 24 months. Hardly surprising given the improved consumer engagement that results from the technology. But more than that, the challenging economic climate is seeing healthcare organisations looking for better ways to make processes more efficient, enhance their existing products and services and lower costs. The key to this is AI that brings with it a more innovative environment to automate manual, error-prone processes and introduce a sophisticated layer of analytics that can deliver new insights to the wealth of data already available. These platforms use algorithms and machine learning to analyse and interpret data, while empowering the healthcare organisation with the means to provide more personalised customer experiences.

Piotr Orzechowski, CEO at Infermedica explains why AI provides so many advantages over traditional rule-based decision trees and how integrating AI technology with doctors expertise can enhance patient care. Anxiety about the introduction of automation in the workplace has been well documented. With some estimations stating that, by 2025, machines will be doing half of all work tasks, perhaps concerns are not entirely misplaced. In healthcare, robotics and machine learning are already having an impact on patient care, providing basic assistance across many clinical facilities – no doubt a life-saving job. Some may feel that eventually technology – specifically Artificial Intelligence (AI) – will progress to a point where it can deliver advanced consultation, without the need for a physician.

These data, if harnessed appropriately, could enable health-care providers to target the causes of ill-health and monitor the effectiveness of preventions and interventions. For this reason, policy makers, politicians, clinical entrepreneurs, and computer and data scientists argue that a key part of health-care solutions will be artificial Intelligence (AI), particularly machine learning.

Advanced technologies have caused a significant impact on the development of the healthcare industry. Artificial Intelligence (AI) and Machine Learning (ML) in particular, have allowed for significant breakthroughs in life science and healthcare research and treatments, whether that's automating critical but repetitive tasks to free up time for clinicians, through to automatic speech recognition for faster disease diagnosis, or the ability to create synthetic controls for clinical trials. But with 75 per cent of healthcare enterprises planning to execute an AI strategy next year, there's a far greater opportunity round the corner to further unleash its potential. Here, six experts from leading healthcare organisations including Brainomix, AiCure, HeartFlow, Cambridge Cognition, Oxford Brain Diagnostics and Zebra Medical Vision, share their views on what 2020 holds for the industry. "As highlighted earlier this year, the NHS aims to become a world leader in AI and machine learning in the next five years. In 2020, we expect to see this become more apparent in practical terms with, AI technologies becoming the predominant driving force behind imaging diagnostics. With around 780,000 people suffering a stroke each year in Europe, and 7.4 million people living with heart and circulatory diseases in the UK, it is imperative we find ways to reduce the burden on healthcare organisations and improve time to disease detection. The number of MRI and CT scans for example is already on the rise, and AI has the ability to read scans as accurately as an expert physician. Utilising these new technologies to review scans for any disease can reduce patient wait time and ease the burden on medical staff. There will be greater recognition next year of the value of AI in augmenting human performance."