Artificial Intelligence (AI) is one of the most talked-about technologies of the last decade. It saw more deployment initially in other industries – telecoms and financial services (FS) were amongst the early adopters - but the past few years have seen a number of life sciences firms embrace AI and reap some of the benefits.Just look at the impact AI and machine learning are expected to have on healthcare, in accelerating and transforming patient diagnoses. Investments in healthcare-focused AI start-ups have increased hugely, and in life sciences, the opportunities are not dissimilar.
AI promises to make clinical trials cheaper, faster and more targeted and in an age of data overload, AI offers a way to find and keep teams focused on what’s important - from what’s being said in the market, to how drugs are designed and developed.
What does AI do differently?
AI takes automation and makes it smart. Where robots in factories excelled at doing repetitive, mundane tasks efficiently and tirelessly, with precision, AI can be programmed to carry out more complex tasks.
Machine learning improves upon that, allowing AI-based systems to find better ways of doing things. Rather than humans having to foresee every possibility and programme a system for every eventuality, AI-based systems can learn and adapt from what they know to create effective and powerful shortcuts.
If they’re faced with a deluge and range of data that would tie up a human team for days or months, machine learning systems can perform analyses and distil subtle trends that humans might overlook. In the context of pharmacovigilance they can help scour the internet for relevant patient feedback about life sciences products, or identify unmet needs or gaps in the market.
An obvious area where AI and machine learning can help is in managing matters of regulatory compliance – where requirements are multiplying and changing all the time. With each new regulatory initiative or submissions hoop that companies need to jump through, the business agility and creativity they are aiming for appears to become further out of reach.
The AI ideal is that Regulatory Affairs teams’ product lifecycle content systems will make it more intuitive to manage data changes, document authoring and reviews, quality control, and submission. Currently much of this is managed via comprehensive rules, templates and workflow which help to streamline processes and ensure that the right data is used in support of the given requirement.
But what if AI and machine learning could promote reliable shortcuts, and issue red flags or suggestions if rogue actions are taken, the wrong master data is used, or someone tries to alter approved ISO IDMP-compliant source content?
The AI benefits
As well as freeing up skilled people’s time to do more satisfying and productive work, AI could also reduce the risk of dependence on a single person’s knowledge of how things are done. It’s easy to see how companies would benefit from this, as it getting harder to attract experienced skills for important Regulatory roles as demand increases yet the pressures of the job take their toll.
AI could also transform regulatory information and submissions management transformation - improving the process of planning, structuring, authoring, publishing and archiving content. Example scenarios might include using AI to monitor and determine which content elements of a submission are routinely included, so that they become a structural component in their architecture.
AI can reinforce compliance and content quality along the supply chain, too, helping to restrict what country affiliate representatives are able to do with content. Where there have been quality violations, AI could provide the analysis and insight so teams can act and prevent repeated issues.The drivers for change are very clear – from the inefficient management and strategic use of operational data, to the high cost of developing medicines and preparing them for market – and the benefits even clearer. Now is truly the time for life sciences firms to truly embrace AI.