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Artificial Intelligence


AI For Commercial Life Sciences: 3 Trends You Can’t Ignore In 2020

   by Rasim Shah    95
AI For Commercial Life Sciences: 3 Trends You Can’t Ignore In 2020

As we enter a new decade, our belief in the the impact of Artificial intelligence (AI) is only getting stronger. Supporting the industry to drive the right drug to the right patient at speed is a huge responsibility that we take very seriously. Towards the end of the last decade we have seen great progress made within life sciences and the use of AI, but moving into 2020 the spotlight on commercial teams and gaining competitive advantage with AI will intensify.

ConstruQt – The Beginnings of the Chemical Data Revolution

   by Peter Jarowski    136
ConstruQt – The Beginnings of the Chemical Data Revolution

Chemical Data Has Problems

The state of data access, quality and dissemination in Chemistry is extremely poor - so poor that it is blocking advances in machine learning (ML) and artificial intelligence (AI), and also impeding research and development in traditional methods. The recent surge in AI skepticism is a direct consequence of years of over-hype and promises based on precarious data. Over-the-top expectation were offered without enough consideration for the data quality and volume required to train fancy algorithms. The old adage “^&$% in, ^&$% out” holds true (we can say ‘crap’ right?). This opinion is in line with recent statements by the CEO of Novartis, for example, who runs the second largest pharmaceutical company in the world, lamenting the difficulty in accessing quality datasets to make AI effective.

6th Digital Pathology & AI Congress

   by BiopharmaTrend    96
6th Digital Pathology & AI Congress

This Congress has a reputation for advancing pathology practice by exploring the implementation of digital pathology and artificial intelligence to advance patient care.

40+ presentations over two days offer the opportunity to discover more about the latest advances and applications of digital pathology. Learn how artificial intelligence and machine learning is being applied to primary diagnosis and clinical research and how the image-based information environment is transforming the laboratory.

6th Digital Pathology & AI Congress

   by BiopharmaTrend    93
6th Digital Pathology & AI Congress

This Congress aim to advance pathology practice by exploring the implementation and use of digital pathology and artificial intelligence to advance patient care.

40+ presentations over two days will offer the opportunity to discover more about the latest advances and applications of digital pathology. Learn how artificial intelligence and machine learning is being applied to primary diagnosis and clinical research and how the image-based information environment is transforming the laboratory.

The Evolution Of Pharmaceutical R&D Model

   by Andrii Buvailo    2001
The Evolution Of Pharmaceutical R&D Model

There is a plethora of analytics reports, including ones by Deloitte, DKV Global, and Ernst and Young, all pointing out to a declining business performance of the pharmaceutical industry. They all convey a similar bottomline message: the decline is not due to a lack of innovation (the innovations are growing). And not because sales are falling or markets are shrinking (revenues are growing in general, and the markets are expanding with the expanding and ageing population). The key reason of the declining financial performance is the fact that research and development (R&D) costs are growing substantially faster over an average investment period, than the actual revenues over the same period. This kills operational profits, leading to a decline in the overall business gain. A direct consequence of that -- an increasingly stagnating industry, cutting sometimes promising R&D programs, jobs etc.  

There are two more relevant questions here: 

1) why R&D costs are growing faster than revenues, considering that technological progress is seemingly providing more and more optimal and powerful technologies to pharma companies at a constantly decreasing specific price (e.g. costs of computation, sequencing, screening and many other things are falling), and 

2) what to do about it to reverse the decline in pharma industry performance?