At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.

With the pandemic's negative impact on the global economy, says the firm, technology leaders must assess the emerging opportunities resulting from COVID-19 and provide technological innovations to build company, society, and consumer resilience. Automation/robotics, advanced data analytics, IoT and "sensorization," security and privacy, and business model innovation are seen as the five critical success factors for growth. "From transformative MegaTrends to geopolitical chaos, there are several factors making it increasingly difficult to grow," says Murali Krishnan, Visionary Innovation Group Senior Industry Analyst at Frost & Sullivan. "In the near term, companies should focus on diversifying supply chains and leveraging new opportunities arising from changing customer demands. In the long term, it is important to internally adapt to new technologies that support workplace and operational continuity to have a smoother transformation during recovery."

Precision medicine is a medical model, which proposes customization of the healthcare to a subgroup of patients, based on a genetics, lifestyle and environment. This technique allows doctors and researchers to prognosis treatment and prevention strategies for a specific disease which can work on a group of people. It is opposed to a one-size-fits-all approach, in which disease treatment and prevention techniques are advanced for the average individual with much less attention for the variations among individuals. There is an overlap between the terms "precision medication" and "personalized medicine." As per the National Research Council, "personalized medicine" is a traditional word with a meaning close to "precision medication."

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3

AEYE Health and Optomed have agreed to enter clinical and commercial collaboration to introduce an AI fundus camera Aurora AEYE. The collaboration includes a clinical trial with the aim to receive U.S. FDA approval for autonomous AI for retinal screening. Once the clinical trials are commenced and successfully completed, the Aurora AEYE will include Optomed's handheld fundus camera Aurora and AEYE Health's AI-based retinal screening system that aims to provide analysis of the retina for diabetic retinopathy changes and receive diagnostic results within 60 seconds. The Aurora AEYE simplifies the retinal screening process by providing an easy-to-use retinal screening system to examine these patients outside the ophthalmologist's office, including primary care and endocrinology clinics or pharmacies. Zack Dvey-Aharon, Ph.D., Co-founder and CEO of AEYE Health: "Today, we are taking a big step forward in the direction of providing accurate, affordable and useable solution to detect retinal conditions, prevent blindness and save lives. The use of our advanced AI algorithms and Optomed's quality handheld fundus cameras sets to democratize diagnostic eye screenings and ensure that all patients who need treatment will receive it on time. We are delighted to team up with Optomed and we look forward to spearheading global efforts to develop AI-based solutions for the early detection of a wide variety of retinal diseases."

Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process is stationary. However, in many real-world applications, this assumption is violated, and using existing algorithms may result in a performance lag. To proactively search for a good future policy, we present a policy gradient algorithm that maximizes a forecast of future performance. This forecast is obtained by fitting a curve to the counter-factual estimates of policy performance over time, without explicitly modeling the underlying non-stationarity. The resulting algorithm amounts to a non-uniform reweighting of past data, and we observe that minimizing performance over some of the data from past episodes can be beneficial when searching for a policy that maximizes future performance. We show that our algorithm, called Prognosticator, is more robust to non-stationarity than two online adaptation techniques, on three simulated problems motivated by real-world applications.

One study estimated that pharmaceutical companies spent US$2·6 billion in 2015, up from $802 million in 2003, for the development of a new chemical entity approved by the US Food and Drug Administration (FDA). N Engl J Med. 2015; 372: 1877-1879 The increasing cost of drug development is due to the large volume of compounds to be tested in preclinical stages and the high proportion of randomised controlled trials (RCTs) that do not find clinical benefits or with toxicity issues. Given the high attrition rates, substantial costs, and low pace of de-novo drug discovery, exploiting known drugs can help improve their efficacy while minimising side-effects in clinical trials. As Nobel Prize-winning pharmacologist Sir James Black said, "The most fruitful basis for the discovery of a new drug is to start with an old drug". New uses for old drugs.

Catching abnormalities on a medical image is important, but case backlogs often mean radiologists are cut short on how long they can spend with each one. Enter Aidoc, a 4-year-old Israel-based startup providing artificial intelligence tools for radiologists. The company secured an additional $20 million for its Series B funding led by Square Peg Capital, which initially led the round that began in April 2019. The new funds bring the Series B round to $47 million and gives Aidoc a total of $60 million raised to date, according to Crunchbase data. If the AI detects something, the tools alert the radiologist, Aidoc co-founder and CEO Elad Walach told Crunchbase News. "What has happened in recent history is that scanners have become cheaper, so now there is more imaging, which is overloading a radiologist's workflow," he said.

Computational modelling has been brought under the spotlight during the COVID-19 pandemic, with scientists trying to predict how the SARS-CoV-2 virus will spread. On 23 March 2020 UK prime minister Boris Johnson announced a lockdown to tackle the spread of coronavirus, following the example of other countries around the world who chose this strategy to halt the virus' progression. This decision came days after Johnson's government toyed with the idea of letting the virus spread and infect up to 70% of the population, in order to develop so-called "herd immunity". The stark policy shift left people wondering what had changed. They predicted that should no action be taken, the death toll in the UK could reach 500,000, and may exceed 2 million in the US.

Business leaders no longer think about artificial intelligence in terms of future impact--they're seeing the impact today. AI is appearing in all corners of business, transforming the way companies operate. Health care is no exception. Health care players are using AI to address significant inefficiencies and open up powerful new opportunities. These include everything from the delivery of remote health care services to the early diagnosis of disease and the hunt for new life-saving medicines.