Over the past few decades, data generation has veritably exploded. However, the'Big Data paradigm' is not so much concerned with the volume of that data, but how businesses and, indeed, industries can derive meaningful insights from what has become a glut of information. With the currently popular approach to artificial intelligence (AI) focussing on the Big Data paradigm, also, pharmaphorum spoke with Adityo Prakash, CEO of Verseon, about the whys and wherefores, delving deeper into the processes for dealing with the current mountain of data and how it can be generated, as well as the purposes for which it can be dealt with constructively, and efficiently. "The fundamental underlying assumption is that an enormous amount of data is available to teach an AI programme how to handle the problem at hand," Prakash began. However, he explained, "the number of known examples to train AI is at least many thousands of times larger than the number of variables or features to be tracked."

A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine. Machine learning is a type of artificial intelligence that involves using data to train computers to make decisions and learn from experiences, according to Pharmaphorum. It has the potential to cut costs of research and development for drugmakers by helping researchers to predict what will and won't work in clinical trials. However, the report says a lot of the data being used in drug development is not suitable for machine learning purposes. There is a phenomenon known as "garbage in, garbage out," where a machine learning system can't produce credible results because of poor data, according to Pharmaphorum.

Imagine you're in the emergency room, where doctors and nurses are always making last minute critical decisions. To be able to have a trusted system that you could have a dialogue with, that you could argue with, will help you make more informed decisions. It's not going to make your decision for you, but it's going to help you reason more effectively. The reasoning side of AI is becoming increasingly important. When we brought Watson and other solutions to market, narrow AI was an emerging technology. With narrow AI you can quickly get very good results from a thin slice of data, but narrow AI can be very complex as well.

The UK government has announced £250m of funding for the NHS to set up a national artificial intelligence (AI) lab to enhance care and research. Announcing the funding, new prime minister Boris Johnson said the lab will work on ways to use AI to improve the detection of diseases and automate admin tasks to free up staff to care for patients, amongst other things. "The NHS is leading the way in harnessing new technology to treat and prevent, from earlier cancer detection to spotting the deadly signs of dementia," Johnson said. Health secretary Matt Hancock added that the NHS was "on the cusp of a huge health tech revolution that could transform patient experience by making the NHS a truly predictive, preventive and personalised health and care service". Earlier this year the Topol Review of the training needs of the future NHS workforce recommended an increase in the number of clinicians trained to use digital, AI and robotics technologies.

Eleven years is a long time to be writing about'digital pharma', and I do indeed remember when it was all My First Twitter Account and iPhone Apps as far as the eye could see. Over those years some technologies have entered the digital health mainstream, while others have failed to live up to their promise, never emerging from what Gartner's Hype Cycle terms the Trough of Disillusionment. Stuck in those doldrums we might find, amongst others, Google Glass, Google's smart contact lens and Nokia's digital health ambitions, but one tech sector that is living up to the hype is artificial intelligence (AI). It has certainly featured on its fair share of'next big thing in health tech' lists over the years, but the last few months have seen a real sense of momentum build around the area. To stick with Gartner's Hype Cycle model for a moment, AI in pharma and healthcare is being rapidly driven up by what the consultants term the Slope of Enlightenment and towards the Plateau of Productivity. The headwinds for this were certainly there in 2018, when all the signs were that AI in pharma was on the rise.

Digital technology is playing an increasingly important role in healthcare, and in the pharma industry as it searches for ways to find new drugs, while cutting costs and reducing expensive trial failures. The UK government's life sciences tsar, Sir John Bell, set the tone for 2018, when he went on record to say that artificial intelligence (AI) could save billions of pounds for the NHS. Bell told the BBC that researchers at an Oxford hospital have AI technology for diagnosing heart disease that could shave £2.2 billion from the NHS' pathology spend. Another AI system developed by a company called Optellum could allow more than 4,000 cancer patients a year to be diagnosed earlier. This could save £10 billion if adopted in the US and EU, according to the company's science and technology officer, Dr Timor Kadir.

Bayer is looking to accelerate its patient safety data monitoring by using artificial intelligence in a move it hopes will allow any potential drug-related side effects to be detected much earlier. The German pharmaceutical company has tied up with professional services firm Genpact for the work, signing a multi-year agreement for its Pharmacovigilance Artificial Intelligence (PVAI) products. These will be added to Bayer's existing pharmacovigilance database and IT systems and, the firm said, will strengthen its focus on patient safety. Michael Levy, global head of pharmacovigilance for pharmaceuticals and consumer health at Bayer, said: "With Genpact, we have found a partner whose innovative capabilities in the area of applying advanced AI and machine learning technologies to pharmacovigilance provide us with an opportunity to further increase the efficiency of our pharmacovigilance operating model and case processing, while maintaining our high quality and compliance standards." Genpact said its PVAI solution, which extracts adverse event data from source documents in an automated fashion, has taken part in, and won, a number of competitive proof-of-concept trials run by large pharmaceutical companies.

The Scripps Research Translational Institute is partnering with graphics firm NVIDIA to develop AI and deep learning best practices, tools and infrastructure to develop AI applications using genomic and digital health sensor data. With NVIDIA, California-based research organisation Scripps will establish a centre of excellence for artificial intelligence in genomics and digital sensors. Scripps and NVIDIA will work to advance the use of machine learning and deep learning to harness the exploding quantity of health data. The partnership will focus on data generated by faster, more affordable genome sequencing gear, and digital health sensors such as smartwatches, blood pressure cuffs and glucose monitors. NVIDIA AI experts and Scripps researchers and clinicians will use deep learning and machine learning, to tackle the deluge of genomics and sensor data.

A pioneering artificial intelligence (AI) computer test can help detect whether individuals are in the early stages of dementia. Through a series of questions with an interactive on-screen avatar, a computer programme designed by researchers from Osaka University and Nara Institute of Science and Technology, can discern whether responses given by individuals indicate cognitive problems. The team behind the research was seeking simple alternatives to the medical imaging used by hospitals to pick up the disease, particularly as rates of the illness soar. They also wanted to lower the chance of people becoming used to being asked the same set of questions by doctors determining whether someone is showing signs of dementia. Through algorithms, the programme assesses gaze, delay in responses, intonation, the percentage of verbs and nouns used, voice articulation and recall.

We are in the midst of a significant shift in pharmaceutical drug development, with many leading companies focusing increasingly on orphan drugs targeted at small niche markets. An orphan disease is a medical condition or disorder that affects less than 200,000 people in the US. The National Institutes of Health (NIH) has classified as many as 7,000 medical conditions as orphan diseases. Although a rare disease affects only a small population, the collective number of rare diseases affects as many as 25 million people. This mammoth-sized number of people requiring niche drugs is a serious public health concern.