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AI’s potential in the pharma life cycle

From the acceleration of regulatory submissions – by identifying data gaps that have led to delays or rejections in the past – to the transformation of the conduct of clinical trials and patient safety monitoring, artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. () has substantial potential to change the way life sciences organisations operate.

and machine have risen rapidly up the business agenda in a wide range of industries – during the last year in particular. On the basis that computers can analyse and interpret data far more quickly and holistically than humans can, market innovators are staking their reputations on the breakthroughs that those analyses and interpretations will enable – ranging from improved customer self-service to advanced problem-solving in such areas as health diagnosis and predictive maintenance. It isn’t just that machines return results at higher speeds or that they can work around the clock; machines also learn extremely efficiently so that their performance improves exponentially over very short periods of time.

Getting closer to patients

One of the more exciting options is that can take pharmaceutical companies deeper into the realms of wellness and the proactive prevention of illness – especially self-inflicted health problems – as technology learns how to recognise warning signs and then prompt better decisions or timely interventions. At a time that the future of life sciences is being hotly debated, the opportunity for the industry to refine and expand its role is an important one to take advantage of. Back-end technology already exists to facilitate more intelligent and proactive health monitoring by taking things forward as drug companies rely on finding the optimum ways for patients to interact with and use the tools.

Proactive intervention

Already is being used to identify women whose Twitter posts indicate they may have increased risk of developing two relatively rare diseases: ovarian cancer and cervical cancer. And that application has produced accurate alternative diagnostic insights. Elsewhere, companies are attempting to aggregate data from patient health records by way of -derived ontological approaches that try to extract useful data from handwritten doctors’ notes.

Increasing regulatory submission success

On a more traditional, operational basis, offers a path through the data complexity that has typically held back life science organisations from becoming more agile, innovative and responsive. By uncovering gaps in the data, machine can detect regulatory dossier anomalies that might be holding up product approval. With what could be tens of thousands of pages making up a submission, it can be hard for a human to manually spot where data or metadata may not be stacking up. An system that is taught to assess previous, successful submissions can quickly learn patterns and alert regulatory teams if it detects anomalies with the latest submission and, based on past experience, can analyse whether a drug is likely to become authorised. […]

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