BiopharmaTrend.com

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.

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.

/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.

The application of artificial intelligence (AI) in the pharmaceutical industry has become a long-term strategic priority for most companies. However, the efficiency of this endeavor depends greatly on the availability of large volumes of properly curated quality data, which is not always the case.

While pharma organizations generate huge volumes of data across all stages of drug discovery, development, and commercialization, not all types of data are equally useful for building efficient machine learning (ML) pipelines. For instance, it is relatively easier to apply AI-tech to consumer-related business processes, where lots of well-understood and properly labeled data is available, than it is for basic research tasks, where data is complex, often poorly labeled and extremely domain-specific.

The above situation leads to a faster pace of progress with AI application in such areas as financial analysis, consumer-behavior prediction, patient classification, marketing, and so on.

One of the important hurdles that pharma companies are trying to solve using AI tech is brand management. Indeed, understanding peculiar features of various patient categories, their purchasing behaviors, reactions to different products, revealing possible risks and side-effects for each class -- those things become essential for pharma companies to be able to develop and implement truly patient-centric brand management strategies. Luckily, this is one of the most fruitful areas for the application of machine learning (especially deep learning) models.

To get a better understanding of how it can be done, I have asked several questions to Agnieszka Wolk, Senior Director, Data Science, IQVIA, who recently presented this topic at the PMSA 2019 European Summit in Basel, Switzerland. . 

 

In this interview, Rasim Shah, Director at OKRA Technologies provided a glimpse into how the company applies state of the art machine learning technologies to solve real world challanges in the life sciences. Rasim also agreed to answer several questions about a more general context of AI in pharma, its current challanges and future perspective, as well as describe the current efforts the European Union puts into supporting the AI ecosystem in the region.

 

Rasim Shah, Director at OKRA Technologies:

OKRA Technologies is a leading European artificial intelligence (AI) company for life sciences. Our goal is to empower life science executives at their desks or whilst on the move, with explainable AI outputs. OKRA’s solutions deliver suggestions, predictions and explanations to enable life sciences executives and operational teams to drive the right drug to the right patient with humanised and understandable AI outputs. The OKRA engine learns from real-world data, structured, unstructured, clinical, commercial and scientific literature to drive the right insight to the different teams in life sciences. Our deep expertise in AI, combined with in-depth medical and product knowledge from life science leaders, has allowed us to develop and co-create products that can transform the way life sciences approach traditional industry challenges. We focus on operationalising AI in an ethical way by placing users of these systems at the centre.