Artificial Intelligence (AI) in Life Sciences Roadblocks and Current Applications

Artificial Intelligence (AI) is by far the most talked about technological advancement of this decade. Although the idea of AI has been around for years, the processers and computational speed of the previous era were not up to the mark, thus couldn’t support the analysis of huge data as required. But with the evolution of new generation algorithms and systems, the computational speeds have risen by multiple folds, uncovering the scope for real-time analyses of huge data banks. What makes AI so exceptional is its adoption of predictive patterns in place of reactive patterns of conventional systems. With an aim to bank on the new-age technology potential, almost every industry has adapted to AI and integrated it into their processes to reduce routine and repetitive work. Despite such huge potential, Life Sciences industry, so far, has been one of the least beneficiaries of this boom. But why?  

Roadblocks for AI in Life Sciences:

The unique component of AI is in its continuous learning systems (CLS) algorithm, which provides users the data that would normally be tough to perceive in a short period of time. It keeps analyzing the past results, deviations and corrections, and a best match for a given situation to improve the accuracy of the future results. In a nutshell, it learns and improves on the go. But that would require massive amounts of historical data from which the AI can interpret and predict. However, the data, nowadays, is protected by privacy protection laws (e.g. General data protection regulation (GDPR) of EU). Assuming, if the government policies allow usage of existing user data in near future, as long as the data is from a single source, the results of the analysis will be divergent.

Although health agencies like US FDA have introduced laws such as 21st Century Cures Act to help accelerate innovation in medical devices manufacturing, drugs, and biologics development and clinical trial designs, new technologies might grow beyond the scope of existing regulations. This might trigger the need for Regulatory reform on a regular basis. But clearing legislative permissions usually takes years. A huge waiting period can be the result and the benefits would be delayed to end-users.

Added to these roadblocks, the diminishing end-user trust is also a concerning factor for manufacturers. There shall be devices that diagnose, detect and advise patients on further course of action to reduce the ailments. But if the user is unwilling to have faith in it, then the primary purpose of the product would be unmet. However, boosting user confidence with the needed evidence can take a long time. There is also a shortage of skilled personnel that have experience in understanding both life sciences and technological processes. To train Regulatory experts for technological expertise or vice versa would be an arduous task.

Feasible Solutions:

The journey of AI is projected to be very dynamic. So, Regulatory agencies and industry must act together and change as per the evolving needs. Life sciences organizations must collaborate with each other while developing the AI integrated systems to maximize their accuracy. Also, on the Regulatory front, the onus lies on health authorities to evolve and initiate more novel Regulatory approaches that shall regularize the advanced tools in a streamlined manner.

Current Applications for AI in Life Sciences:

Currently, the transition from reactive to predictive systems is under progress. As part of the shift, organizations are using AI for some crucial aspects of product development. Few of the current applications are listed below.

  • Identification of novel indications for medicines with available user data in large volumes
  • Identification of compounds that can be combined for treatment of new indications or increased efficiency
  • Decreasing the errors in diagnosis
  • Increasing the efficiency of clinical trial design
  • Data management within the limits of data privacy and protection laws
  • Discovery of inherent deficiencies in drug discovery process and the usage of AI to counter them

Though the prevailing uses of AI are merely a tip of the iceberg, AI in its full potential has the ability to boost life sciences light years ahead. How far has your organization realized and benefitted from this burgeoning technology? Compare and transform your products and Regulatory operations to the next level of automation. Be compliant.

Tags: Life Sciences; Artificial Intelligence, AI; USFDA; EMA; Regulatory Affairs; GDPR; Automation, Technology, 21st Century Cures Act