Collaborating Authors

Pharma and AI? Let's try augmented intelligence first


Like smartphones upended everyday tasks from communication to shopping, artificial intelligence promises to change how the pharmaceutical industry discovers drugs, carries out R&D and even commercializes products. Before that happens, though, the industry needs to figure out precisely what AI means and how much investment will be required. Further challenges include trepidation about how best to prove it works and confusion over its power. According to a survey of over 12,000 participants conducted by consultancy PwC in 2016, lack of trust and a need for the human element were the biggest hurdles to using AI in healthcare. "I don't think artificial intelligence is really here; it's more augmented intelligence.

Where are the opportunities for medtech and pharma in 2020?


It's that time when we start to look ahead to what next year holds for the life science sector...Lu Rahman outlines 2020s big medtech players A decade ago the healthcare advances create by AI would have seemed the stuff of dreams. But back in 2018 Theresa May announced plans to use artificial intelligence and data to transform the way certain diseases like cancer. The technology is moving at a pace – this year we heard that a team led by the University of Surrey had filed the first ever patent for inventions autonomously created by AI without a human inventor. Professor Ryan Abbott explained the implications this had for the life science sector: "These filings are important to any area of research and development as well as any area that relies on patents. Patents are more important in the life sciences than in many other areas, particularly for drug discovery. These tasks can be the foundation for patent filings. "As AI is becoming increasingly sophisticated, it is likely to play an increasing role in R&D including in the life sciences.

AI: the smart money is on the smart thinking - PMLiVE


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.

Data and culture as stepping stones for Digital@Scale


Increasingly, we are seeing the importance that well-implemented digital and analytics strategy can have at a company, including positive impact on a company's bottom line. However, it has been a challenge for organizations to shift from individual digital initiatives that have proven value, to scaling them into broader initiatives that have benefits across the business. Companies looking to implement and scale a digital strategy have increased their focus on technological capabilities. However, while this continues to be an area that many companies struggle with, successful scaling encompasses more than adding technological capabilities. In 2015, when we ran our first European roundtable on digital in pharma and medtech, participants were still discussing whether digital was here to stay and how to adopt it.

Artificial intelligence in drug discovery and diagnosis - Pharmaphorum


Machine learning is widely predicted to make drug discovery and patient diagnosis quicker, cheaper and more effective in the future, and signs of this can already be seen. Nearly 70 years ago, artificial intelligence researchers at New Hampshire's Dartmouth College discussed building machines that could sense, reason and think like people -- a concept known as'general AI'. But their plans were destined to remain in the land of science fiction for quite some time. However, in the last decade the rapid growth in computer-processing power, the availability of large data sets and the development of advanced algorithms have driven major improvements in machine learning. AI researcher Ben Goertzel brought to light'narrow AI' in 2010.