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Part 1: The Growing Momentum of Machine Translation in Life Sciences

This first post in an ongoing series takes a closer look at the emerging use and acceptance of machine translation (MT) in the Life Sciences industry. We take a look at the expanding role MT is likely to have in the industry over the coming years and explore some key use cases and applications.

The Life Sciences industry, like every other industry today, feels the impact of the explosion of content and of the driving forces that compel the industry to use MT and machine learning (ML). The growth is caused by:

  • The volume of multilingual research impacting drug development
  • The increasing volume of multilingual external consumer data now available (or needed), which influence drug discovery, disease identification, global clinical research, and global disease outbreak monitoring

Consumers share information in many ways, across a variety of digital platforms. It has become increasingly necessary to monitor these platforms to stay abreast of trends, impressions, and problems related to their products.

It is useful to consider some of the salient points behind this growing momentum.

MT use has exploded

The content that needs translation today is varied, continuous, real-time and always flowing in ever greater volumes. We can only expect this will continue and increase.

The use of global public MT portals is in the region of an estimated 800 billion words a day. This is astounding to some in the localization industry who account for less than 1% of this, and it suggests that MT is now a regular part of digital life.

Everyone, both consumers and employees in global enterprises, use it all the time. This use of public MT portals also involves many global enterprise workers, who may compromise data security and productivity by using these portals. However, the need for instant, always-available, translation services is so urgent that some employees will take the risk.

Some large global enterprises recognize both the data security risks entailed by this uncontrolled use and the widespread need for controlled and integrated MT in their digital infrastructure. In response, they have deployed internal solutions to meet this need in a more controlled manner.

Why Life Sciences has not used MT historically

There are several reasons why Life Sciences has not used MT, including quality requirements, lags in technical adoptions, global need and non-optimized MT capabilities.

The Life Sciences industry needs high quality, accurate translations given that often the life and death of human beings could be at stake if a translation is inaccurate, creating a subject-matter-expert-dependent and verified quality mindset. The industry saw little benefit from using MT since it was so hard to control and optimize. Depending on the kind of errors, there can be catastrophic consequences from failures and thus a general “not good enough for us" attitude within the industry. Occasional breaking news about MT mishaps did not help.

The Life Sciences industry is not typically early adopters of new technologies.Historically Life Sciences organizations have focused technology and innovation in targeted areas but that is changing as the need to innovate in multiple areas is only increasing to stay competitive. It is no longer a nice to have, it’s a must have. At the same time, technologies like machine translation have evolved and improved significantly over the last few years which has impacted how MT is viewed. Machine Translation is now seen as a viable and effective solution to address certain global content challenges.

There’s a concern about risk management / mitigation. Life Sciences organizations have been concerned about the risk involved in leveraging machine translation due to the data security aspects as well as the ability to handle their industry specific terminology requirements. Generic MT solutions like Google do not provide adequate data security and tailoring controls for the specific needs of an enterprise. Once something is translated using Google Translate it is potentially available in the public domain. Data privacy and security is a top priority for Life Sciences companies and the need for an Enterprise MT solution that provides the benefits of MT technology but with the necessary security elements is essential. Additionally, there were many use cases where the enterprise needed to have the MT capabilities deployed in private IT environments, and carefully integrated with key business applications and workflow.

But compelling events are forcing change..

The massive increase in the volume of content in general and high volumes of multilingual content from worldwide digitally connected and active patients and consumers are key drivers for the enterprise adoption of MT across the industry.

 

In the Life Sciences industry, an exponential increase in internal scientific data (particularly in genomics and proteomics data) has triggered global research. This research has led to new ways to develop drugs, knowledge about disease pathways and manifestation, and to the development of tailored treatments for individual patients. Keeping abreast of potentially breakthrough research, much of which may be in local languages has become a competitive imperative.

The huge increase in patient-related data such as the data from central laboratories, prescriptions, claims, EHRs and Health Information Exchanges (HIEs) provides an immense opportunity to analyze and gain insights across the entire value chain, such as:

  • Drug Discovery: Analyzing and spotting additional indications for a drug, disease pathways, and biomarkers
  • Clinical Trials: Optimizing clinical trials through better selection of investigators and sites, and defining better inclusion and exclusion criteria
  • Wearables: Wearable technologies generate a significant amount of data to monitor patients, such as tracking key parameters and therapy compliance
  • Aggregated data: The ability to aggregate data from multiple reporting sources has also increased the volume and flow of such data

The Impact of Social Media

Signals related to problems and adverse effects may appear in any language, anywhere in the world. The need to monitor and understand this varied data grows in importance as information today can spread globally in hours. Safety concerns can have serious implications for patient health and on a company’s financial health and reputation. These concerns need to be monitored to avoid derailing a drug that may be on track to become an international success.

Additionally, another important use for machine translation is in the social media and post marketing area. Life Sciences organizations can compile large amounts of data from multiple languages leveraging MT technology. Monitoring sentiment across all language groups allows Life Sciences organizations to track market-specific issues, sentiment and explain trends. It also helps develop marketing and communication strategies to handle dissatisfaction and avoid crises or to build further momentum to ride positive sentiment.

Applying MT to Epidemic Outbreak Predictions

ML and AI technologies are also applied to monitor and predict epidemic outbreaks around the world, based on satellite data, historical information on the web, real-time social media updates, and other sources. For example, malaria outbreaks predictions take into account temperature, average monthly rainfall, the total number of positive cases, and other data points.

Increasingly the aggregated data that makes this possible is multilingual and voluminous and requires MT to enable more rapid responses. Indeed, such monitoring would be impossible without machine translation.

In part 2, we’ll explore recent breakthroughs in MT, and how they are changing the game for Life Sciences firms. And over the coming months, we’ll delve deeper into the many different ways that Life Sciences companies can use MT within their complex organizations.