In our next Fireside Chat, Natalie Yeadon will sit down with Dr. Andree Bates, Founder & CEO of Eularis, to discuss how Eularis helps healthcare teams utilize AI and "Future Tech" to solve their biggest commercial challenges and deliver measurable growth. Among many other interesting topics, they will dissect the barriers to the adoption of "Future Tech"; their predictions for the roles that AI will play in healthcare, Pharma, and medical research within the next few years; and how AI be leveraged to improve patient-centricity. Don't miss out on this exciting Fireside Chat.

AI could also have a transformative effect on clinical decision-making through the utilisation of the huge levels of genomic, biomarker, phenotype, behavioural, biographical and clinical data that is generated across the health system. Bayer and Merck & Co provide a perfect example of this. They have developed an AI software system to support clinical decision-making of chronic thromboembolic pulmonary hypertension (CTEPH) – a rare form of pulmonary hypertension. The software helps differentiate patients from those suffering with similar symptoms that are actually a result of asthma and chronic obstructive pulmonary disease (COPD), and therefore diagnose CTEPH more reliably and efficiently. The CTEPH Pattern Recognition Artificial Intelligence obtained FDA Breakthrough Device Designation in December 2018.

From the acceleration of regulatory submissions - by identifying data gaps that have led to delays or rejections in the past - to the transformation of the conduct of clinical trials and patient safety monitoring, artificial intelligence (AI) has substantial potential to change the way life sciences organisations operate. AI and machine learning have risen rapidly up the business agenda in a wide range of industries - during the last year in particular. On the basis that computers can analyse and interpret data far more quickly and holistically than humans can, market innovators are staking their reputations on the breakthroughs that those analyses and interpretations will enable - ranging from improved customer self-service to advanced problem-solving in such areas as health diagnosis and predictive maintenance. It isn't just that machines return results at higher speeds or that they can work around the clock; machines also learn extremely efficiently so that their performance improves exponentially over very short periods of time. Those are some of the reasons that science documentaries and news reports have begun focusing on the potential for AI and machine learning to facilitate, for instance, earlier medical diagnoses - particularly in complex or baffling cases.

A rare opportunity now exists for the pharmaceutical industry to play its part in driving customer behaviour with a digital marketing approach that the world is fast becoming familiar with, and will soon be reliant on - artificial intelligence (AI). An industry on the cusp of change driven by customer behaviour Few would question that pharmaceutical marketing is a niche sector with a unique set of obstacles for those tasked with delivering results. It's an environment that challenges the perceived (marketing) wisdom of how to reach, engage and influence customers with innovative and stimulating content and messaging. Our sector can be a restrictive, hazardous environment and we are challenged to define and deliver our work using a set of principles established in more conventional or conducive marketing environments. Understandably we follow, rather than lead, marketing conventions, latching on to terminologies, principles and methodologies that we (and our customers) see outside our industry every day.

Gartner Says Worldwide Server Revenue Declined 5.8 Percent in the Third Quarter of 2016; Shipments...

Across healthcare and pharmaceutical development, AI brings with it the possibilities of significant improvements through marginal gains. The key to generating the maximum value of the technology is to identify where real problems lie and where real business opportunity exists. A prescription for big data Labelling data as'big' doesn't really stand up to scrutiny anymore. When the first commentators were discussing the phenomenon, the data issue still only resonated with a few international conglomerates. However all this has changed and now the velocity with which data is being created means that the very data issue itself is well beyond'big'.

What is AI? AI is concerned with replicating mechanisms of human intelligence using computers and software. One popular technique involves replicating the brain's neural network (a modelling technique known as'artificial neural networks') to analyse information, extract layers of detail from within it and ultimately attempt to interpret the results. This makes the technology perfect for performing tasks such as analysing language and identifying objects within images. The basic principles have been around since the '60s and were refined in the '90s to allow systems to'learn' based on previous results. In medicine such methods were used to perform tasks such as analysing pap smears.

What is AI? AI is concerned with replicating mechanisms of human intelligence using computers and software. One popular technique involves replicating the brain's neural network (a modelling technique known as'artificial neural networks') to analyse information, extract layers of detail from within it and ultimately attempt to interpret the results. This makes the technology perfect for performing tasks such as analysing language and identifying objects within images. The basic principles have been around since the '60s and were refined in the '90s to allow systems to'learn' based on previous results. In medicine such methods were used to perform tasks such as analysing pap smears.

The healthcare sector has always embraced technology. Since the advent of the computer, technologists and healthcare professionals have been working together to exploit technological breakthroughs so that they might improve patient outcomes while also minimising costs and delivering high standards of care to a greater number of patients. When a technology becomes reliable, cost-effective and scalable, it is embraced and generally thrives. We saw this in the '70s with the adoption of mainframe computers, in the '80s with the widespread adoption of personal computers and local networking, in the '90s with internet-based systems and more recently with the adoption of mobile technologies. It appears now we are on the cusp of the next technological revolution within healthcare, combining the vast amounts of data available, cloud computing services and machine learning techniques in order to create artificial intelligence (AI)-based solutions that can provide expert insight and analysis on a mass scale, at a relatively low cost.