How can femtech enhance women's special journey to delivery? How can it help them start that journey when they're having difficulties in conceiving? A common understatement that you may hear these days is that the world has been affected by the Pandemic. Let's get something straight, the world has not merely been "affected" by the Pandemic, it has completely transformed, or let's say, tele-transformed. The Pandemic has had an obscene amount of negative consequences on the world, yet, dare we say it, there are a few players that have somewhat benefitted from the situation, players that encourage and believe in virtual, digitally-powered transactions.
Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.
Each year, national and local governments determine the relative priorities of services to allocate funding. How would AI spend the cash? What makes more sense for a vibrant society -- spending on economic development or growth, spending on education, international development, social care, libraries? What would ideal balance look like? If this question sounds familiar, it's not the first time we've tried to apply artificial intelligence (AI) to making this decision.
A policy regulating the use of artificial intelligence (AI) in healthcare has been launched in Dubai. AI in healthcare includes the use of robots to perform surgery faster and assist the surgeon more efficiently; and data-based analysis of health information. Dr Mohammad Al Redha, director of Health Informatics and Smart Health Department at the Dubai Health Authority (DHA), said AI refers to systems or devices that simulate human intelligence to perform tasks with the use of data. The policy aims to determine regulatory requirements for the provision of AI solutions healthcare. It lays down the ethical requirements and defines the main roles and responsibilities of stakeholders.
The pandemic has put a spotlight on how big data and analytics technologies are being used in the public health sector. Contact tracing, where phone numbers and location data from mobile devices were combined with lab results in public health systems to issue alerts when an individual came in contact with a confirmed COVID patient. This information empowered people to preemptively self-isolate and/or head for rapid testing. Google and Apple, meanwhile, developed some groundbreaking application programming applications (APIs) for contact tracing that protected anonymity, while allowing their devices to receive updates from state disease surveillance systems and send out alerts. The use of big data during the pandemic is certainly a harbinger of things to come, and public health agencies must understand how such data is being used.
Praduman Jain is CEO and founder of Vibrent Health, a digital health technology company powering the future of precision medicine. There has been quite a bit of hype over the last several years about how artificial intelligence (AI) would transform health care. Translating the predictive power of AI algorithms into research methods and clinical practice, however, has proved challenging, which inevitably leads to disillusionment. But rather than getting frustrated with AI and machine learning, I would argue that strategic and ethical deployment of artificial intelligence will, by necessity, be central to the success of precision health research over the next decade. Several factors are coming together to make AI more critical to progress.
Investments in artificial intelligence and machine learning are finally on the rise in healthcare. While the industry has been slow to adopt AI in comparison to other sectors like financial services and manufacturing – with 70% of health systems yet to establish a formal program – a recent survey found that 68% of health system executives plan to invest more in AI in the next five years to help reach their strategic goals. And the investments are expected to be significant; the global AI in healthcare market size is estimated to reach $120.2 billion by 2028. The opportunities for AI in healthcare are widespread, spanning both operational and clinical use cases including fraud prevention, voice-assisted charting, registration, remote patient monitoring and more. AI holds particular promise for connected medical devices and telehealth – an integral part of the Internet of Medical Things (IoMT) – as it enables faster triage, intake, detection and decision making.
The first barrier is data availability. ML and deep learning models require large datasets to accurately classify or predict different tasks.27 Sectors where ML has seen immense progression are those with large datasets available to enable more complex, precise algorithms.28 In healthcare, however, the availability of data is a complex issue. On the organizational level, health data is not only expensive,27 but there is ingrained reluctance towards data sharing between hospitals as they are considered the property of each hospital to manage their individual patients.29
The Government has said that artificial intelligence (AI) in GP practices will help manage patients in the elective care backlog. It today announced that new technology and innovation will allow the NHS to treat 30% more elective care patients by 2023/24. It added that NHS'come forward with a delivery plan for tackling the backlog'. In March, NHS England suggested that GPs could be asked to review hospital waiting lists for elective care to help prioritise and manage patients from the following month. Details were limited, but NHS England later told GPs that they must'jointly manage' patients stuck in the backlog of care caused by the Covid pandemic with hospitals. Meanwhile, Pulse revealed in June that NHSX and NHS England were considering the viability of a wider roll out of an artificial intelligence triage model based on that used by Babylon.
LUMO Labs is backing Dutch health-tech startup Autoscriber, which specializes in artificial intelligence (AI)-supported voice recognition software that can interpret healthcare consultation between doctors and patients, according to a Tuesday (Sept. "The problem Autoscriber is solving is universal: reducing time and money spent on repetitive, administrative tasks by physicians while increasing transparency, comprehensibility and human interaction in deeply personal treatment situations," LUMO Labs Founding Partner Andy Lürling said in the release. He added that Autoscriber's solution "is dynamic and highly scalable." The funding for Autoscriber is being provided by LUMO Labs investment arm LUMO Fund II TTT AI. The company is backing Autoscriber because the concept can "dramatically improve the lives of caregivers and patients," according to the release.