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Artificial Intelligence


6th Digital Pathology & AI Congress

   by BiopharmaTrend    27
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    26
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    1618
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? 

19 Online Marketplaces Facilitating Life Science Research

   by Andrii Buvailo    16760
19 Online Marketplaces Facilitating Life Science Research

(Last updated: 23.08.2018)

Online marketplaces are websites with a “many-to-many” business logic. They can host multiple suppliers trading with multiple buyers via different e-commerce tools available as a part of a website functionality.

Why are online marketplaces great?

Online marketplaces can provide a substantial added value to its users. For example, buyers can quickly compare and select better offerings without the need to research multiple websites and surf online for price comparisons or product specifications. Additionally, marketplaces bring more transparency, trust, and standardization to the whole process of sourcing.

Current State of AI in Pharma: Key Achievements Beyond Hype

   by Andrii Buvailo    2935
Current State of AI in Pharma: Key Achievements Beyond Hype

/Last update -- 24 Dec 2019/

A background context -- opportunities and challenges

Current widespread interest towards artificial intelligence (AI) and its numerous research and commercial successes was largely catalyzed by several landmark breakthroughs in 2012, when researchers at the University of Toronto achieved unprecedented improvement in the image classification challenge ImageNet, using their deep neural network “AlexNet” running on graphics processing units (GPUs), and when that same year Google’s deep neural network managed to identify a cat from millions of unlabeled Youtube videos, representing a conceptual step in unsupervised machine learning.